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Identi�cation of Characteristic User Behavior with a
Simple User Interface in the Context of Space Heating
Erkennung des spezi�schen Nutzerverhaltens mittels einfachem
Nutzerininterfaces im Rahmen der Raumbeheizung
Von der Fakultät für Maschinenwesen der Rheinisch-Westfälischen
Technischen Hochschule Aachen zur Erlangung des akademischen Grades
eines Doktors der Naturwissenschaften genehmigte Dissertation
vorgelegt von
Michael Adolph
Berichter: Univ.-Prof. Dr.-Ing. Dirk Müller
Univ.-Prof. Dr. rer. nat. Martin Frank
Tag der mündlichen Prüfung: 03. November 2017
Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek online verfügbar
Bibliographische Information der Deutschen Nationalbibliothek Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb-nb.de abrufbar. D 82 (Diss. RWTH Aachen University, 2018) Herausgeber: Univ.-Prof. Dr.ir. Dr. h. c. Rik W. De Doncker Direktor E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate (EBC) E.ON Energy Research Center Mathieustraße 10 52074 Aachen E.ON Energy Research Center I 53. Ausgabe der Serie EBC I Energy Efficient Buildings and Indoor Climate Copyright Michael Adolph Alle Rechte, auch das des auszugsweisen Nachdrucks, der auszugsweisen oder vollständigen Wiedergabe, der Speicherung in Datenverarbeitungsanlagen und der Übersetzung, vorbehalten. Printed in Germany ISBN: 978-3-942789-52-3 1. Auflage 2018 Verlag: E.ON Energy Research Center, RWTH Aachen University Mathieustraße 10 52074 Aachen Internet: www.eonerc.rwth-aachen.de E-Mail: [email protected]
Abstract
Increasing the energy efficiency of buildings is a promising approach to reduce
a society’s energy consumption. This work shows an approach to automati-
cally detect user preferences on room temperature and the thermal behavior of
a building solely on feedbacks provided by the user. This information is used
to create a temperature schedule for each individual room. If the user is not
present, the room’s temperature is automatically reduced to save energy. Prior
to the user’s expected return the temperature is raised again. This automated
approach allows the user to save energy without compromising his thermal
comfort and the need for complex schedule programming.
This work evaluates the suggested base algorithm with simulations conducted
in Modelica and a field test. The user characteristics are estimated with three
different versions of a learning algorithm. To determine the user’s preferences,
two different user characteristics are used. The effects of three different build-
ing standards are compared. Also different approaches to add a necessary pre-
heating period are tested. Additionally, the effects of different heating systems
are evaluated. The results are evaluated by thermal comfort, energy consump-
tion and the number of feedbacks, i. e. the user’s effort. The field test puts the
algorithm in a real life scenario and tests its general functionality and its ease of
use, using sensor data and a questionnaire.
It is found that energy savings of 10-20 % are possible without sacrificing the
user’s thermal comfort. All this is possible with an easy to use system that relies
only on occasional qualitative feedback.
Zusammenfassung
Die Energieeffizienz von Gebäuden zu erhöhen ist ein vielversprechender Ansatz,
um den Energieverbrauch zu verringern. Diese Arbeit zeigt einen Ansatz, der
die Nutzervorlieben bezüglich der Raumtemperatur erkennt. Zusätzlich wird
das thermische Verhalten des Gebäudes analysiert. Mittels dieser Informatio-
nen wird ein Temperaturzeitplan für jeden einzelnen Raum erstellt. Die Raumtem-
peratur wird bei Abwesenheit automatisch abgesenkt, um Energie zu sparen.
Vor Rückkehr wird die Temperatur wieder angehoben. Dieser Ansatz erlaubt
dem Nutzer, Energie einzusparen ohne Kompromisse beim Komfort eingehen
zu müssen oder komplexe Zeitpläne zu programmieren.
Die Arbeit untersucht den vorgeschlagenen Algorithmus mit Simulationen in
Modelica und einem Feldtest. Die Nutzervorlieben werden mit drei unterschiedlichen
Lernvarianten erkannt. Zwei unterschiedliche Nutzerverhalten werden verwen-
det. Zusätzlich wird der Algorithmus mit drei unterschiedlichen Dämmstan-
dards überprüft.
Um den thermischen Komfort des Nutzers sicher zu stellen, werden verschiedene
Methoden zum Vorheizen getestet und der Einfluss des Heizsystems auf das
Ergebnis überprüft. Die Bewertung erfolgt anhand des Komforts, des Energiebe-
darfs und der Anzahl an Rückmeldungen. Der Feldtest überprüft die Funktion-
alität des Algorithmus und seine Nutzerfreundlichkeit in einem realen Szenario.
Im Ergebnis sind Energieeinsparungen von 10-20 % möglich ohne den thermis-
chen Komfort des Nutzers einzuschränken. Dabei wird ein einfach anzuwen-
dendes System genutzt, dass lediglich gelegentliches, qualitatives Nutzerfeed-
back benötigt.
iii
Contents
Nomenclature vii
List of Figures x
List of Tables xv
1 Motivation 1
2 State of the art 5
2.1 Effectiveness of intermittent heating . . . . . . . . . . . . . . . . . . 5
2.2 User Interaction with the heating system . . . . . . . . . . . . . . . 6
2.2.1 Conceptional limitations of thermostats . . . . . . . . . . . 8
2.2.2 Usage of MCUs . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.3 Usage of PCUs . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Design principles for User-Interfaces . . . . . . . . . . . . . . . . . 13
2.4 Requirements for an improved space heating control system . . . 15
3 Methodology 17
3.1 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.1 Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.2 Preheating methods to improve the user experience . . . . 23
3.1.3 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2.1 User Presence . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.2 User Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.3 Simulating the thermal environment . . . . . . . . . . . . . 54
iv
3.2.4 Using an alternative heating system . . . . . . . . . . . . . . 56
3.2.5 Evaluation Methods . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.6 Naming Conventions for Simulations . . . . . . . . . . . . . 63
3.3 Field test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4 Simulated Results 67
4.1 Results of the Reference Cases . . . . . . . . . . . . . . . . . . . . . 67
4.2 Results of the base algorithm with two different user types . . . . . 74
4.2.1 Evolving profile and temperature set-points . . . . . . . . . 75
4.2.2 Simulated temperatures vs. temperature set-points . . . . . 82
4.2.3 Detailed user feedback . . . . . . . . . . . . . . . . . . . . . . 84
4.2.4 Profile detection by the algorithm . . . . . . . . . . . . . . . 87
4.3 Adding pre-heating sequences . . . . . . . . . . . . . . . . . . . . . 90
4.3.1 Pre-Heating with a temperature slope . . . . . . . . . . . . . 91
4.3.2 Pre-Heating with an increased temperature block . . . . . . 97
4.3.3 Pre-heating with an adaptive estimation of parameters . . 104
4.4 Comparison of the different pre-heating measures . . . . . . . . . 112
4.5 Effect of an increased heating power on the algorithms performance116
4.6 Effects of an increased radiative ratio . . . . . . . . . . . . . . . . . 122
4.7 Effects of different learning methods . . . . . . . . . . . . . . . . . 129
4.8 Effects of different insulation standards . . . . . . . . . . . . . . . . 132
4.9 Independence from initialization . . . . . . . . . . . . . . . . . . . 134
4.10 Independence from user models . . . . . . . . . . . . . . . . . . . . 141
5 Results from a �eld test 145
5.1 Results from measurements . . . . . . . . . . . . . . . . . . . . . . . 145
5.2 Results from user questionnaire . . . . . . . . . . . . . . . . . . . . 148
6 Conclusion 153
7 Outlook 157
v
Bibliography 160
A Further Results 171
A.1 Result tables for low and high energy demand scenarios . . . . . . 171
A.2 Results overview for high and low energy demand scenarios . . . . 178
A.3 Devlopment of the user Feedback . . . . . . . . . . . . . . . . . . . 181
A.4 Temperature distributions and curve-fitting results . . . . . . . . . 182
B Dynamic Time Warping 185
vi
Nomenclature
Abbreviations
a. u. arbitrary units
DTW Dynamic Time Warping
FT Field test
HVAC Heating, Ventilating and Air Conditioning
MCU Manual Control Unit
PCU Programmable Control Unit
PTRV Programmable thermostatic radiator valve
TRV Thermostatic radiator valve
TRY Test Reference Year
TUS Time Use Survey
UI User Interface
Symbols
Q̇ Power
Θ Heaviside-function
µ Mean
µ Mean
π persistency parameter
vii
σ Standard deviation
σ Standard deviation
pi,min Minimum probability value to make the persistency parameter π ap-
plicable
sph, min Lower bound for pre-heating slope before Tmin is increased
sph Slope of the pre-heating ramp in K/h
Tin Indoor temperature
Tmin Lower boundary for indoor temperature to ensure the ability to reheat
a room on time
tph, max Upper boundary for the duration of the pre-heating block before Tmin
is increased
tph Duration of the preh-eating block
v Skewness of a skewed normal distribution
v Skewness of a skewed normal distribution
k number of pre-heating events
l number of successful pre-heating events
n Number of rooms in the flat
Q Energy Demand
r random number
Codes used for configurations of simulation
+H Indicates usage of a heating system with increased power as compared
to the standard cases
+R Indicates usage of a faster heating system with higher radiative ratio
viii
[0-9] Value of the slope in 0.1 K/h if pre-heating is S, or time in hours if pre-
heating is B
A Pre-heating times are calculated adaptivly
B Pre-heating based on a preceding temperature block
C Cluster Learning
D (first position) Daily Learning
D (second position) User feedback based on the model according to Daum
E Energy efficient behavior in the reference case
H High energy demand simulation environment
L Low energy demand simulation model
M Medium energy demand simulation environment
N No pre-heating
P User feedback based on the PPD
R Reference case
S (fourth position Pre-heating with a slope
S (second position) Standard behavior in the reference case
W Weekly Learning
Subscripts
air Indicating air temperatures
conv convective
ph Indicates values within a pre-heating phase
rad radiative
sim Indicating simulated/measured values, as opposed to set-point values
ix
List of Figures
2.1 Examples of TRVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Energy efficiency of the user interaction with thermostats . . . . . 10
2.3 Reproduction of a graph of key requirements for a system to be
acceptable as shown in Peffer et al. [2011] . . . . . . . . . . . . . . . 14
3.1 General concept of the temperature detection algorithm. . . . . . 18
3.2 Flow chart for the algorithm’s feedback scheme. . . . . . . . . . . . 19
3.3 Flow chart for the Simulated Annealing algorithm. . . . . . . . . . 21
3.4 Flow chart of the pre-heating algorithm. . . . . . . . . . . . . . . . 23
3.5 Effects of the pre-heating algorithm . . . . . . . . . . . . . . . . . . 24
3.6 Effects of the second version of the pre-heating algorithm. . . . . 26
3.7 Illustration of the different learning concepts. . . . . . . . . . . . . 28
3.8 Influence of the different learning classes on a generic tempera-
ture profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.9 Flowchart to calculate the pre-heating parameters sph and tph . . 32
3.10 Flowchart for the calculation of the minimum temperature. . . . . 34
3.11 Graphical overview about the different parts of the learning algo-
rithm and the simulation test environment. . . . . . . . . . . . . . 36
3.12 Flow chart for the interaction between simulation and algorithm. 38
3.13 Flow chart to create the occupancy profile for one user. . . . . . . 41
3.14 Occupancy profile used as input for the simulated user behavior. 43
3.15 Flow chart to rate thermal comfort and estimate the user action
based on Fangers PMV and PPD method . . . . . . . . . . . . . . . 46
x
3.16 Probability distribution to feel cold, warm or comfortable for six
subjects, based on data by Daum et al. [2011]. . . . . . . . . . . . . 48
3.17 Flow chart to rate thermal comfort and estimate the user action
based on Daum’s comfort measuring method. . . . . . . . . . . . . 51
3.18 Alternative relationships between PMV and PPD found by Araújo
and Araújo [1999]; Yoon et al. [1999]; Mayer [1997]; de Paula Xavier,
Antonio Augusto and Lamberts [2000]. . . . . . . . . . . . . . . . . 53
3.19 Layout of the apartment and boundary conditions. Heat exchange
takes place between the different rooms of the apartment and the
outside. The walls to adjacent flats are adiabatic. . . . . . . . . . . 56
3.20 Temperature set-point profiles for the two reference cases. . . . . 60
4.1 Simulated air temperature of the reference cases in the living room
for medium energy demand. . . . . . . . . . . . . . . . . . . . . . . 68
4.2 Deviation of the air temperature from its set-point for all rooms
for medium energy demand . . . . . . . . . . . . . . . . . . . . . . . 69
4.3 Simulated air temperatures for the different reference cases when
the user is present . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.4 Effect of the algorithm on the initial profile . . . . . . . . . . . . . . 75
4.5 Effect of a given user feedback on the temperature profile. . . . . . 76
4.6 Feedbacks provided by the user to the system for medium energy
demand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.7 Feedbacks provided by the user to the living room . . . . . . . . . 79
4.8 Distribution of the setpoint temperatures for the algorithm with-
out pre-heating measures. . . . . . . . . . . . . . . . . . . . . . . . . 81
4.9 Difference between simulated temperature Tmeas and set-point
temperature Tset for the living room and medium energy demand 82
4.10 Distribution of the simulated temperatures for the algorithm with-
out pre-heating measures. . . . . . . . . . . . . . . . . . . . . . . . 83
4.11 Feedbacks given by the user to the system. . . . . . . . . . . . . . . 85
xi
4.12 Static presence profile to assess the algorithm’s general ability to
detect the user behavior. . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.13 Temperature set-point distribution . . . . . . . . . . . . . . . . . . 89
4.14 Successful presence and absence matching for the user’s used through-
out the simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.15 Temperature set-points for the slope based pre-heating method . 92
4.16 Temperature set-point distributions for the slope based pre-heating
for the medium energy scenario . . . . . . . . . . . . . . . . . . . . 93
4.17 Distributions of the simulated temperatures for the cases with and
without slope based pre-heating. . . . . . . . . . . . . . . . . . . . . 94
4.18 Development of the user feedbacks for simulations using slope
based pre-heating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.19 Temperature set-points with block based pre-heating algorithm . 98
4.20 Temperature set-point distribution for the block based pre-heating
method and the medium energy scenario. . . . . . . . . . . . . . . 99
4.21 Distributions of the simulated temperatures for cases with and
without pre-heating. . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.22 Energy consumption and comfort for the block based pre-heating
method (Daum) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.23 Energy consumption and comfort for the block pre-heating method
(PPD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.24 Changes in slope and pre-heating time due to the adaptive pre-
heating method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.25 Changes in the minimum temperature for the living room. . . . . 107
4.26 Comparison of the temperature set-points for the different pre-
heating methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.27 Comparison of the simulated temperatures for the different pre-
heating methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.28 Comparison of Simulation Results (Daum) . . . . . . . . . . . . . . 113
4.29 Comparison of Simulation Results (PPD) . . . . . . . . . . . . . . . 115
xii
4.30 Differences in slope and pre-heating time between standard and
higher powered case. . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.31 Differences in Tph, min between standard and higher powered case. 118
4.32 Comparison of times of low comfort for the Daum user for the
normal and higher powered heating system. . . . . . . . . . . . . . 121
4.33 Heating Power of the standard heating system and the fast, radia-
tive heater. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.34 Air and surface temperatures of the rooms depending on the heat-
ing system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4.35 Dyamic pre-heating parameters for the fast, radiative heater. The
fast, radiative heater allows for shorter pre-heating times, espe-
cially for the Daum user. . . . . . . . . . . . . . . . . . . . . . . . . . 125
4.36 Development for the minimum temperature for the fast, radiative
heater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.37 Valid Feedbacks over time for the Daum user in the *DM-SA cases. 129
4.38 Valid Feedbacks over time for the PPD user in the *PM-SA cases. . 130
4.39 Presence matching between base simulation and the same simu-
lations using a different initialization for the Daum user . . . . . . 136
4.40 Presence matching between base simulation and the same simu-
lations using a different initialization for the PPD user . . . . . . . 137
4.41 Weekly DTW difference between the setpoint temperatures for the
Daum user. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.42 Weekly DTW difference between the setpoint temperatures for the
PPD user. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.43 Comparison of the energy demand of the different simulations for
the Daum user . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
4.44 Comparison of the energy demand of the different simulations for
the PPD user . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.45 Comparison of the duration of low thermal comfort for the Daum
user with different initialization values . . . . . . . . . . . . . . . . 140
xiii
4.46 Comparison of the duration of low thermal comfort for the PPD
user with different initialization values . . . . . . . . . . . . . . . . 141
4.47 Relationship between feedback probability and PMV . . . . . . . . 142
4.48 The algorithms ability to match the user’s state for six different
user types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.1 Temperatures with and without the algorithm in a study. . . . . . . 146
5.2 Temperatures with and without the algorithm in the living room
of a flat. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
5.3 Ease of use of the adaptive heating system. . . . . . . . . . . . . . . 148
5.4 Comprehensibility of the adaptive heating system. . . . . . . . . . 149
5.5 Times of feeling too cold or too warm for both heating systems. . 151
A.1 Comparison of Simulation Results (Daum), low energy demand . 178
A.2 Comparison of Simulation Results (PPD), low energy demand . . 179
A.3 Comparison of Simulation Results (Daum), high energy demand . 179
A.4 Comparison of Simulation Results (PPD), high energy demand . . 180
A.5 Feedbacks given over the time of the simulation for the clustered
learning method without pre-heating for both behavioral models
and low energy demand . . . . . . . . . . . . . . . . . . . . . . . . . 181
A.6 Feedbacks given over the time of the simulation for the clustered
learning method without pre-heating for both behavioral models
and high energy demand . . . . . . . . . . . . . . . . . . . . . . . . 182
A.7 Temperature set-point distributions for six different user types and
the fitted skewed normal distribution . . . . . . . . . . . . . . . . . 183
xiv
List of Tables
3.1 Values for metabolic rate and clothing factor in dependence of
user’s state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Fitting Parameters for Daum’s thermal comfort model according
to Daum et al. [2011] . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3 Different parameterization of the three different thermal environ-
ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4 Nominal power of the heating system for the standard and fast
radiative heater system. . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 Size, inhabitants and occupation of the field test participants . . . 65
4.1 Summary of the results for the reference case for the standard be-
havior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2 Summary of the results for the reference case for the efficient be-
havior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3 Results of the base case . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.4 Results of the simulations using the first pre-heating method . . . 96
4.5 Results for the CDM-B* cases . . . . . . . . . . . . . . . . . . . . . . 101
4.6 Results for the CPM-B* cases . . . . . . . . . . . . . . . . . . . . . . 102
4.7 Results of the dynamic pre-heating methods for the medium en-
ergy scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.8 Results for the higher powered heating system (Daum) . . . . . . . 119
4.9 Results for the higher powered heating system (PPD) . . . . . . . . 122
4.10 Results for the fast, radiative heater and Daum user . . . . . . . . . 128
4.11 Results for the fast, radiative heater and PPD user . . . . . . . . . . 128
xv
4.12 Results of the different learning methods for the *DM-SA cases . . 131
4.13 Results of the different learning methods for the *PM-SA cases . . 131
4.14 Results for the CD*-*A cases . . . . . . . . . . . . . . . . . . . . . . . 133
4.15 Results for the CP*-*A cases . . . . . . . . . . . . . . . . . . . . . . . 133
4.16 Relative energy savings compared to the RS* cases . . . . . . . . . 134
A.1 Result table for the CDL-N and CPL-N cases . . . . . . . . . . . . . 172
A.2 Result table for the CDH-N and CPH-N cases . . . . . . . . . . . . 172
A.3 Result table for the CDL-S5 and CPL-S5 cases . . . . . . . . . . . . 173
A.4 Result table for the CDH-S5 and CPH-S5 cases . . . . . . . . . . . . 173
A.5 Results for the CDL-B* cases . . . . . . . . . . . . . . . . . . . . . . 174
A.6 Results for the CPL-B* . . . . . . . . . . . . . . . . . . . . . . . . . . 174
A.7 Results for the CDH-B* cases . . . . . . . . . . . . . . . . . . . . . . 175
A.8 Results for the CPH-B* cases . . . . . . . . . . . . . . . . . . . . . . 175
A.9 Results for the *DL-SA cases . . . . . . . . . . . . . . . . . . . . . . . 176
A.10 Results for the *PL-SA cases . . . . . . . . . . . . . . . . . . . . . . . 176
A.11 Results for the *DH-SA cases . . . . . . . . . . . . . . . . . . . . . . 177
A.12 Results for the *PH-SA cases . . . . . . . . . . . . . . . . . . . . . . . 177
A.13 Fitting parameters for skewed normal distribution with µ the dis-
tributions mean, σ its standard deviation and v its skewness. . . . 184
xvi
1 Motivation
Reducing energy demand is a major concern as many methods of power gener-
ation emit greenhouse gases. These anthropogenic greenhouse gases are “ex-
tremly likely to have been the dominant cause of the observed warming since
the mid-20th century” and the “continued emission of greenhouse gases will
cause further warming and long-lasting changes in all components of the cli-
mate system, increasing the likelihood of severe, pervasive and irreversible im-
pacts for people and ecosystems” [IPCC, 2014, pp.4, 8]. A large amount of en-
ergy mankind consumes is utilized to heat or cool our buildings. In Germany,
25 % of the end energy is consumed in private households, 74 % of this energy
is used for space heating. In total, 30.4 % of the 9310 PJ of energy consumed in
Germany in 2010 were used for space heating [Ziesing, 2013, pp. 16, 26]. This
gives energy efficiency measures in buildings a high leverage to decrease the
global energy consumption. Energy efficiency measures for buildings may in-
clude measures to improve the thermal envelope of the buildings (e. g. lower
thermal conductivity) and using more efficient heating and cooling methods
like heatpumps or solar power. Additionally the control systems may be im-
proved by adding model predictive control or including weather forecasts.
Despite the improvements in the energy efficiency of buildings, savings are of-
ten not as high as expected. This can be partially attributed to user behavior.
That users don’t realize the theoretical savings may be due to their conscious
decision to increase their level of comfort at the cost of an increased energy
consumption. This behavior is known in literature as “rebound effect” [Haas
et al., 1998; Calì et al., 2016]. User behavior in general is an important parame-
ter in the energy consumption of buildings as shown by different authors. Dia-
1
1 Motivation
mond [1984, p. F-53] reports that the energy consumption in identical buildings
even with similar residents can vary significantly. Gas consumption for heating
purpose can be as much as four times higher than the average. Differences in
total energy demand by the factor of two have been reported by Lindén et al.
[2006, p. 1918]. The one parameter found most relevant for the increase in en-
ergy consumption of a building is the indoor temperature, which can be easily
influenced by the user. While Palmborg [1986, p. 646] attributes 15±5 % of to-
tal variation in energy consumption to indoor temperatures, Haas et al. [1998,
p. 201] found a linear relationship between indoor temperature and energy con-
sumption. Nevius [2000, p. 8.242] expects a decrease in energy consumption by
about 3 % per ◦F lower indoor temperature, which translates to approximatly
6 % per ◦C. A difference of 39 % between the simulated demand and the mea-
sured consumption of low energy buildings due to higher than expected indoor
temperatures was reported by de Meester et al. [2013, p 314]. Energy consump-
tion as much as three times above the average was attributed to higher indoor
temperatures and longer opening times for windows [Calì et al., 2016, p. 1155].
The strong correlation between energy consumption of a building and indoor
temperature has been shown in a simulation study by Lomas and Eppel [1992,
p. 34] that found energy consumption of a building is most sensitive to indoor
temperatures.
The high influence of the user on the energy consumption may not always be
due to a conscious decision. Also disregard for energy efficient behavior and
problems in using complex control systems may negatively impact the energy
consumption of a building. An example for a not energy efficient behavior are
people, who do not adjust their thermostatic valves very often. Especially when
leaving for work or going to bed, they do not reduce the valve’s set point al-
though this could result in high energy savings [Carlsson-Kanyama et al., 2005;
Carlsson-Kanyama and Lindén, 2007; Karjalainen, 2009; Peffer et al., 2011]. To
allow for automatic savings at night or when leaving for work, programmable
thermostats were developed. These enable scheduled heating: reducing room
2
1 Motivation
temperatures at night or when at work. As the reheating of the room can start
earlier than the users arrival, the comfort is also increased. Nonetheless, these
systems add a lot of complexity to the original system, forcing the user to man-
ually program his schedule, often with a small display, cryptic labels and small
buttons. The possible energy savings correspond to a higher level of complex-
ity and expect higher user interaction as the schedule has to be adjusted on a
regular basis to achieve the highest possible energy savings. These advanced
systems may be inaccessible to the user or too laborious to maintain, and in
the worst case even both. The topics about user behavior and complexity of
control systems will be covered in more detail in the state of the art section of
this work.
A conscious decision to use more heat to improve ones comfort can not be
solved by technical measures and should generally be accepted, as comfort is
one of the main reasons why buildings are built. But using more energy be-
cause saving energy is too laborious or too difficult should not be accepted and
may be solved by technical measures. Repetitive tasks, like reducing temper-
ature set-points at night, may be automated. Difficulties with control systems
can be reduced by improving the user interface (UI) or simplifying the usage of
the control system.
As the indoor temperature has high influence on a buildings energy consump-
tion, reducing the average indoor temperature is a reasonable measure to re-
duce total energy consumption. But if the main reason for creating buildings
is increasing thermal comfort, the lower indoor temperature should not impact
the user. Therefore it makes sense to reduce the temperature while the user is
absent and provide the high temperature only when the user is present. This
work will present basic design principles how user preferences can be detected
from a simple feedback without the need for complex user interaction or ini-
tial programming. These preferences are used to predict future behavior and
adapt room temperatures accordingly. Such an approach will reduce the need
for repetitive tasks and simplify the interaction with the control system, while
3
1 Motivation
maintaining the user’s comfort.
4
2 State of the art
It was stated in the previous section that inefficient user behavior and difficul-
ties with using control systems may result in higher energy consumption than
necessary. This chapter will first establish the fact that intermittent heating is
a reasonable method to reduce total energy demand. Afterwards it summa-
rizes reasons why users act inefficiently and which problems arise from com-
plex control systems. The chapter concludes with showing measures to im-
prove current control systems for space heating to increase user satisfaction
and energy efficiency. At the end the basic design principles for developing
User-Interfaces are described.
2.1 E�ectiveness of intermittent heating
A central claim of this work is that lowering the room temperature when the
user is absent can significantly reduce the energy consumption of a building
while simultaneously not impacting thermal comfort when the user is present.
This section will summarize findings that support this claim.
Lowering temperature for some time will lower the average indoor tempera-
ture, which has been found to be a relevant parameter for building energy con-
sumption. For example, Karjalainen and Lappalainen [2011, p. 941] see the
largest saving potential in the discontinuous use of buildings. The influence of
an occupancy controlled HVAC system is confirmed by Yang et al. [2016, p. 641].
But the finding by Masoso and Grobler [2010, p. 176] that HVAC systems are the
5
2 State of the art
most energy consuming devices left running if a room is not occupied, shows
that there is additional potential to save energy if rooms are not occupied.
One method to heat based on expected occupancy are programmable control
units (PCU) that change the temperature set-point according to a previously
programmed schedule. The effect of PCUs on energy demand is inconsistent,
RLW Analytics [2007, p. 1] report a decrease in natural gas consumption by 6.2 %
due to PCUs and the resulting more energy efficient set-points (lower temper-
atures in winter, higher in summer than the control group). Nevius [2000, p
8.240] estimates the effect in the range of 10 % lower and 5 % higher heating in-
tensity. One reason for the modest result is seen in the reported room tempera-
tures which are similar to the room temperatures of users with manual control
units (MCU) or even slightly higher [Nevius, 2000; Shipworth et al., 2010, 8.242].
This discrepancy may be due to the fact that the participants in the study by
RLW Analytics [2007] volunteered and were keener on achieving energy savings
than in the other studies. Another reason was pointed out by Nevius [2000] and
Peffer et al. [2011]: Saving energy with a PCU requires an active user. People
who use MCUs efficiently will have a low saving potential as their behavior is
already efficient. People who do not care about efficiency will probably not put
in the necessary efforts needed to make efficient use of the PCUs, which also
results in a low saving potential.
It can be summarized that intermittent heating is a method to reduce energy
consumption of a building but the current technologies like PCUs fail to com-
pletely seize the potential.
2.2 User Interaction with the heating system
As saving energy is possible due to intermittent heating it is of interest how peo-
ple interact with their heating system and if they utilize intermittent heating.
User may interact with their heating or air conditioning system through the
6
2.2 User Interaction with the heating system
(a) A simple TRV (b) A programmable TRV
Figure 2.1: Examples of TRVs
control unit. The control unit may be one wall-mounted panel that controls
the whole building or each room can be controlled individually. With respect
to water-based radiator systems there are often two systems: One central wall
mounted system to control e. g. heating curve and heating times and addition-
ally thermostatic radiator valves (TRV) that allow for individual control of each
room. Heating systems in the US are different compared to Europe as Meier
et al. [2011, p. 1892] pointed out. Air conditioning is more common in the US
than in (at least Northern) Europe, for example. Therefore research results may
be different but especially with respect to programming advanced features of a
control system, they are expected to be comparable.
Control systems can be very simple thermostats, which only allow for momen-
tarily adjustment of the temperature set-point or more advanced systems that
allow to program a time schedule for the air conditioning system, so called
programmable thermostats. Examples for a manual TRV and a programmable
thermostatic radiator valve (PTRV) are shown in figure 2.1.
Throughout this work the general term manual control unit (MCU) is used for a
thermostat or TRV which keeps the chosen temperature set-point until further
interaction. Programmable Control Unit (PCU) refers to thermostats or TRVs
7
2 State of the art
that allow programming of schedules, so changes in temperature set-point can
occur without direct user interaction. As TRVs are a specific kind of thermostat,
this work will most times refer to the general term thermostat and only to the
specific term TRV if the findings are specific for this kind of thermostat.
2.2.1 Conceptional limitations of thermostats
Inefficient usage of heating systems can be due to conceptional issues about
the way space heating works or difficulties in using these control systems. In
Northern Europe water-based heating systems are very common and the man-
ual TRV is the most common control device. Manual TRVs have the advantage
of being of low cost, durable, easy to use and require neither energy nor main-
tenance. Despite the very limited interaction options (Turning a knob left or
right up to an end stop), people often do not know how far they need to turn a
TRV to achieve the required results. Turning the TRV too far may result in over-
heating and energy waste [Karjalainen and Koistinen, 2007, p. 2885], this also
holds true for other manual thermostats.
Additionally, many user do not know how a thermostat works. According to
Kempton et al. [1992] they use a so called “Valve-Theory” as a mental model
where the thermostat acts as a simple valve for volume control. Based on Kemp-
ton et al. [1992], a mental model is the user’s idea, of the way a system works in-
dependent of the real way the system works. Mental models are formed by the
user due to his experience with a system. The valve-theory is an example for
a wrong mental model because thermostats control the energy flow according
to a difference between set-point and measured value, while a valve’s energy
flow is independent of the measured value and depends only on the opening of
the valve. Kempton [1986, p. 85] estimates that 25 % - 50 % of the U.S popula-
tion use at least parts of this mental model. The valve theory is an international
phenomenon as also Xu et al. [2009, p. 252] found this mental model in China.
Problems with operating MCUs, because people do not understand how MCUs
8
2.2 User Interaction with the heating system
work, were also found in the UK by Liao et al. [2005, p. 345]. These wrong men-
tal models seem to origin from practical experience [Kempton, 1987, p. 207],
but despite the fact that they are wrong, they often work for the users as they
roughly explain the perceived reactions of the system. Furthermore the mental
model of a heating or cooling valve can result in energy efficient behavior: the
valve theory directly explains the possible energy savings by using a night set-
back, because closing the valve will throttle the energy influx [Kempton, 1986].
But other mental models may hinder energy efficient behavior. Some people
are reluctant to reduce the temperature because they think that energy savings
from a night-setback will be lost when reheating the home [Peffer et al., 2011,
p. 2536].
That complex systems do not achieve their full energy saving potential may be
due to the fact that most people do not choose the most efficient but the eas-
iest way to improve their thermal comfort. If thermal comfort is achieved, the
heating system will be left untouched, even if it is running inefficient [Bordas
et al., 1994, p. 5]. Educating people about the heating system may help, Calì
et al. [2016, p 1151] have shown that feedback given to occupants about their
heating behavior combined with advise how to use the heating system correctly
could result in a lower energy demand.
2.2.2 Usage of MCUs
Not only technical problems may lead to less than optimal results but also con-
venience or misconceptions. Different researchers have conducted several ap-
proaches to gather information, how people actually use thermostats. Figure 2.2
summarizes the results. Due to different research approaches (survey vs. mon-
itoring) and scopes of the research, these results are not directly comparable.
Figure 2.2 groups the different behavior types into three values: efficient usage,
medium efficient usage and inefficient usage, displayed green, orange and red.
Despite differences in the research approaches, figure 2.2 supports the idea that
9
2 State of the art
Figure 2.2: Interaction between users and MCUs as a fraction of the total group.The color indicates the assumed energy efficiency of the behavior.The text shows the behavior as describedb in the original source.
only a minority of people use their thermostats efficiently, while most people
do not actively use a thermostat. Six out of seven works implicate that less than
50 % of the users make use of energy efficient measures as night setbacks. Espe-
cially Kempton et al. [1992] found that approximately one third of the user sets
their thermostat always on maximum. The only exception is the review con-
ducted by Vine [1986]. This research relies on self-reported thermostat settings
which may be prone to a bias towards more energy efficient behavior and wrong
reporting as Kempton and Krabacher [1984, pp. F140-F141, p. F151] pointed
out. Nonetheless, the findings of Vine are confirmed by newer research from
Woods [2006], who states that the average temperature set-points stay constant,
but the individual ones are changed more frequently than assumed. Although
this could indicate energy efficient behavior, it could also be an indication that
the thermostat is more used as a heat or (in case of air conditioning) cold switch
than a thermostat [Kempton, 1987, p. 79].
One reason why people do not interact with the thermostat is the fear to make
a mistake or that they have been told not to touch the thermostat [Kempton
10
2.2 User Interaction with the heating system
et al., 1992, p. 188]. But even if people know how to use a thermostat and that it
is energetically efficient to reduce the temperature set-point on longer absence,
it does not mean, that people would act accordingly. Reasons for this are, e. g.
convenience as people consider it too time consuming and laborious to “adjust
the heat by turning a knob on each radiator” [Carlsson-Kanyama and Lindén,
2007, p. 2169]. Another reason is comfort, as people would like to avoid return-
ing into a cold home [Lindén et al., 2006, p. 1925]. There might also be social
reasons, because a well heated home is an expected social behavior [Wilhite
et al., 1996, p. 798].
Interestingly the willingness to reduce room temperature is not linked to a per-
sonal attitude towards ecological behavior. Carlsson-Kanyama et al. [2005, p. 248]
asked people if they would lower indoor temperatures at night. Rated on a scale
from 1 to 5, where 1 means ‘never’ and 5 means ‘always’ environmental friendly
people scored a mean value of 2.67 compared to 2.34 for the control group. Al-
though the difference between theses scores was significant, both values show
only an average ecological behavior. Further research has shown that the con-
nection between a general environmental friendly attitude and the users be-
havior is weak at best [Nevius, 2000; Lindén et al., 2006]. That there is not nec-
essarily a connection between reported attitude and real behavior was shown
in general by Wicker [1969].
2.2.3 Usage of PCUs
The above raised issues with a lack of thermal comfort on return and the la-
borious part of turning a knob on each radiator could be overcome with the
usage of PCUs. Once a correct schedule is programmed, the PCU will automat-
ically reduce the temperature and reheat the apartment previous to the user’s
return. Of course, if the schedule changes or the behavior deviates from the
normal routine, the schedule must be adapted. But research shows that a PCU
is only a theoretical solution to the problem. As Meier et al. [2011, p. 1893] re-
11
2 State of the art
ported, people tend to use a PCUs like a common thermostat or reprogrammed
the schedule settings rarely (often only once per year). 50 % of the PCUs were
set on a long term hold, reducing the PCUs to manual thermostats1. In low
income houses the long term hold ratio was down to 45 % and the amount of
programmed thermostats was 30 % [Meier et al., 2011, pp. 1893-1894]. So peo-
ple that may be more dependent on lower energy cost, try at least partially to
do so with PCUs. But even if the PCUs were programmed, it does not mean that
the schedules were adapted to the user. 89 % of the users stated that they never
or rarely programmed a weekday or weekend schedule [Meier et al., 2011, p.
1893]. According to Cross and Judd [1997] 61 % of the users use night setback
and 34 % day setback. 15 % keep a constant temperature and 25 % control the
temperature manually. Usage of the programmable features is reduced over
the years, only 51 % of the users use the programmable feature of the thermo-
stat six years after installation [Cross and Judd, 1997]. An important issue on
the effectiveness of PCUs is the conscious decision to buy a PCU [Meier et al.,
2011, p. 1893]. RLW Analytics [2007, p. 25, Figure 9] conducted a survey with
users that bought a PCU for their own. Of these buyers, 17.6 % stated that they
use pre-programmed schedules and 59.5 % use unique schedules.
If one tries to figure out why people do not use PCUs, the answer is usually
because they do not know how to use them. Usage is too complicated and even
simple tasks2 can not be accomplished [Nevius, 2000; Cross and Judd, 1997;
Lindén et al., 2006; Meier et al., 2011]. Obviously an improvement in UI-design
could increase the effective usage of PCUs. RLW Analytics [2007, p. 3] stated
that their improved results compared to previous research may partially be due
to newer PCUs with a better UI.
It can be concluded that neither manual nor programmable thermostats are
used in an effective manner. The two main reasons that hinder efficient us-
1Although it may be argued that PCUs usally use PI controllers and could save energy due to animproved control quality
2e. g. “Identify the temperature the thermostat is set to reach”[Meier et al., 2011]
12
2.3 Design principles for User-Interfaces
age are motivational and conceptual issues. Realizing energy savings with ther-
mostats require user action, a disadvantage true for both types of thermostats
[Peffer et al., 2011]. The motivational issues can be solved by an automation
approach: If people are not willing to spent effort, try to make it effortless. The
conceptual issues can be solved with an improved user interface.
2.3 Design principles for User-Interfaces
As stated before, complex control systems may keep the user from using his
heating system efficiently. As the temperature control system is intended for
a wide deployment, it must be usable for a wide range of persons of differ-
ent age and with different technical understanding. Docampo Rama [2001,
p. 106] identified at least two different generations that differ in the ability to
use modern UI designs. Therefore, the measures that must be taken to reduce
the complexity and necessary knowledge may be different to those groups. A
study conducted by Freudenthal and Mook [2003, p. 55] supports the findings
by Docampo Rama [2001].
Peffer et al. [2011] summarized the key requirements for a system to be accept-
able to the user based on research conducted by Nielsen [1993]. A redrawn pic-
ture of this graph is shown in figure 2.3. It shows that the acceptance of a system
by the user depends partially on the usefulness that is linked to the usability of
a product and requires a system to be easy to learn and to remember, efficient
in its usage, to show only few errors and to be subjectively pleasing.
To make an user interact with a system he must not only have the urge to make
a change but also feel capable to do so. Therefore the user needs to know how
to operate the device and if the adjustment is done right. This reflects the two
requirements “Easy to learn” and “Efficient to use”. The system needs to provide
feedback to the user, so he knows if his action were right. This again makes
the system more easy to learn and it will reduce subsequent errors. Additional
13
2 State of the art
Figure 2.3: Reproduction of a graph of key requirements for a system to be ac-ceptable as shown in Peffer et al. [2011]
feedback is especially necessary if the result of changes that have been made
are delayed by 2-3 hours [Vastamäki et al., 2005, pp. 254-258].
The necessity of a feedback to enable the user to use a system is supported by
different research, conducted e. g. by Karjalainen [2007]; Fountain et al. [1994];
Sauer et al. [2009]; Dale and Crawshaw [1983]. After changing the temperature
set-point there is no immediate change for the user to realize due to thermal
inertia and in contrast to interaction with a light switch, for example. If no feed-
back is given to the user, he is left in the dark if the system tries to comply with
his request. A helpful feedback will result in a higher understanding of the sys-
tem which can result in a better performance, i. e. higher energy savings and
better comfort[Sauer et al., 2009, p. 172].
With respect to heating systems there are several challenges. Besides the de-
layed feedback, the user normally does not know how far a knob must be turned
to achieve the desired effect, as reported independently by Vastamäki et al.
[2005, p. 250] and Karjalainen [2007, p. 2885]. Obviously the user is able to give
14
2.4 Requirements for an improved space heating control system
a qualitative feedback (“‘too cold” or “too warm”) but struggles to give a quan-
titative feedback, i. e. how much too cold or warm it is. Additionally sometimes
wrong ideas persist about good indoor temperatures that may impair the user
to chose the correct indoor temperature if asked for a specific value [Vastamäki
et al., 2005, p.251].
To help the user to interact with a system, Vastamäki et al. [2005, p. 255] suggest
to offer only a few options because this would reduce the total problem space
and the probability to select a wrong solution. Also reducing unnecessary in-
formation will improve the usability because it diminishes the relative visibility
of the relevant information [Karjalainen and Lappalainen, 2011, p. 942]. Having
more options can even result in an irritated user, because typically he has no
insight into the internal function of a system [Karjalainen and Koistinen, 2007,
pp. 2885-2886].
2.4 Requirements for an improved space heating control
system
Literature research has shown that mainly three things may keep a user from us-
ing his heating system efficiently: He does not understand the control system,
he has misconceptions about how efficient usage can be achieved and finally it
takes too much effort to act energy efficient. All three issues must be addressed
to improve current space heating control systems.
Therefore an improved space heating control system should have only as few
options as possible to help the user to understand the control system and in-
teraction should be qualitative instead of quantitative to avoid ambiguities how
much feedback must be given. The easiest thinkable feedback to a heating sys-
tem is “too cold” and “too warm”. With those two quantitative options, all pos-
sible measures of a heating system are covered. Users can easily state if they feel
too warm or too cold, but they have more difficulties in stating by how many de-
15
2 State of the art
grees it is too cold or what exact temperature they would prefer. This inability
makes a more complex input unnecessary. The system should also give direct
feedback to the user about the effect of his actions. Misconception how the
system is operated efficiently is not important if the user gives only feedback
of his thermal sensation and the system decides on its own which measure to
take to improve the thermal sensation3. Automating intermittent heating and
creating heating schedules from user feedback will reduce the user’s effort and
address the third issue. The initial setup can be abolished by using a time- and
room-independent initial profile.
3For a radiator based heating system the options available to the system are very limited. Butalready an ACU gives more options like changing the flow temperature or increasing the fanspeed
16
3 Methodology
This chapter describes the methodology to implement the measures suggested
in the previous chapter into an adaptive algorithm and which methods can be
applied to improve such an algorithm. After explaining the basic concept, the
methods used to test and evaluate the results are introduced. The chapter con-
cludes with a section about naming conventions.
3.1 Algorithm
The central assumption of the suggested adaptive algorithm is that people do
not want to interact with their heating system and that intermittent heating
saves energy. Concerning user interaction, people are especially not willing to
tell the system that they are leaving the building and when they will return.
People will interact with the heating system if they feel uncomfortable. From
this point of view the algorithm must achieve two things: Utilizing the feedback
of the user to predict a temperature schedule for the day and learn for the next
day. These two parts are described in the following two sections. Some ideas of
the algorithm have been previously developed and tested as part of two master
theses by Lupulescu [2011] and Behnke [2014] which have been supervised by
the author of this work.
3.1.1 Adaptation
This section describes the general idea of the algorithm, figure 3.1 shows the
general concept of the algorithm. The upper figure shows the initial tempera-
17
3 Methodology
Tem
per
atu
re in
°C
Leaving for work Return from work
Too Cold Too ColdToo Warm
Temperature profile at start of the adaptive algorithm
Tem
per
atu
re in
°C
Time
Increased temperature due to user feedback
Reduced energy demand due to systematic decreases
Temperature profile learnt from user feedback
Figure 3.1: General concept of the temperature detection algorithm. To coldfeedbacks increase the room temperature in the morning andevening, systematic decreases reduce the room temperature whilethe user is absent.
ture profile with one fixed temperature. The arrows indicate the time the user
gives a manual user feedback, every other time no feedback is given. Also in-
dicated are the times the user is absent. The lower figure shows the resulting
set-point profile at the end of the day. Two “too cold” feedbacks increase the
temperature in the morning and in the evening. Although only one feedback
was given each time, the temperature increase is visible previous and after the
given feedback. While the user was absent, the algorithm lowered the temper-
ature set-points with systematic decreases. The “too warm” feedback given at
18
3.1 Algorithm
Start
Wait for user feedback
Recieved user feedback
Adapt temperature
profile
Systematic temperature decrease
reasonable?
False
TrueWait for next timestep
True
False
Figure 3.2: Flow chart for the algorithm’s feedback scheme. The algorithm waitsone time-step for user feedback. If user feedback is given, the algo-rithm will adapt the temperature profile. If no feedback was givenat the end of the timestep, the system tests if a systematic decreaseis applicable.
the end of the evening lets the temperature set-point decline faster. This figure
shows only the set-point temperatures, differences in measured temperatures
may be occur due to the buildings thermal inertia, the heating system in use
and external effects, e. g. solar radiation.
Based on the idea that the algorithm should rely solely on the user’s feedback
and that the interaction with the system should be as simple as possible, the
feedback options were reduced. The user can tell the system that it is too warm
or too cold. There is neither need to quantify the amount of discomfort nor to
19
3 Methodology
specify a set-point for the temperature. Every day is divided into 144 timesteps,
each of them of 10 minutes length1. A flow chart for this is given in figure 3.2.
After starting the algorithm, it will wait for user feedback. If feedback has been
given by the user, the algorithm will adapt temperatures accordingly, i. e. in-
crease the temperature set-point if it was too cold and decrease it, if it was too
warm. If the user has given no feedback this may have two reasons: The user
may either be comfortable or away2. Both are valid assumption for the user giv-
ing no feedback. The adaptive algorithm handles this ambiguity with a system-
atic decrease. Depending on previous feedbacks and probabilities, a systematic
decrease may be triggered and the temperature is reduced to save energy. If a
systematic decrease is not reasonable, no changes will be made and the algo-
rithm will wait again until the end of the next time-step.
The method to develop the temperature profile adapts some ideas from an op-
timization method called “simulated annealing”. The idea is that in the begin-
ning changes to the profile may be rather aggressive to move fast towards a good
solution. Over time a temperature profile will have emerged, thus the aggres-
sivity of the algorithm is step-wise reduced. In context of a temperature profile
the term aggressivity can translate into four different dimensions: The num-
ber of timesteps the change is propagated forward, the number of timesteps
the change is propagated backward, the value of change in the set-point and
the probability that the action takes place. As an example: On the first aggres-
sivity level there is a 30 % chance that the temperature set-point is changed by
2 K for the current timestep, the following six timesteps and the previous three
timesteps. The next aggressivity level may have a probability of 20 %, a 1.5 K de-
crease and 4 and 2 timesteps propagation forward and backward respectively.
1The decision to use 10 minutes intervals was not made completely arbitrary: The thermostaticvalves used in the field test, described as part of chapter 3.3 on page 64, communicated onlyevery 10 minutes with the control server, thus any higher resolution would not have had anyeffect.
2Precisely speaking, the user may not necessarily be comfortable but at least comfortable enoughto give no feedback, or he is so engaged with a different task that he does not realize his comfortis too low.
20
3.1 Algorithm
Start here if 0 < t < t1
r2 < p2Start here if
t1 < t < t2
r1 < p1
False
r3 < p3Start here if
t2 < t < t3
False
Change profile with aggressivity level 1
Change profile with aggressivity level 2
Change profile with aggressivity level 3
True
True
True
End
Figure 3.3: Flow chart for the Simulated Annealing algorithm. The algorithmstarts always on the left. At the beginning it starts on the top andapplies changes with a high influence on the temperature profile.After some time it will start from a lower starting point, with lessinfluence on the temperature profile.
For the algorithm in use the aggressivity parameters were found by iterative im-
provement. If the aggressivity is too high, the number of necessary feedbacks
increases and the more often occuring systematic set-point decreases may im-
pact the user’s comfort. If aggressivity is too low, the temperature profile will
emerge slowly and more energy is used. The chosen parameters have proven to
be efficient in lowering the temperature in the beginning without deteriorating
a once established profile later on.
Each of the three events (too cold feedback, too warm feedback and no feed-
back) triggers the simulated annealing process at the end of the timestep. It
gives the algorithm information how the temperature profile must be adapted.
21
3 Methodology
This process will take place in the “Adapt temperature profile” box in figure 3.2.
The way the simulated annealing process works is the same for each event, but
the aggressivity levels are parameterized differently as well as the probabilities
pi to pick one of these levels: A too cold feedback must increase the temper-
ature and must ensure that the temperature is increased, while a systematic
decrease may decrease the temperature, but also no effect is acceptable. Fig-
ure 3.3 shows the realization of this method: For the first days, while t < t1, the
algorithm will start on top of figure 3.3. A random number r1 is compared to the
probability p1. If r1 < p1 the parameters of aggressivity level 1 are used and the
process ends, otherwise another random number r2 is drawn and compared to
the probability p2, if r2 < p2, the temperature profile is changed with the pa-
rameters from aggressivity level 2 and the process ends, otherwise the process
continues until ri < pi or if no further level is defined. If each pi < 1, it is possi-
ble that no change to the temperature profile is made.
At a later time t , when t1 < t < t2 the algorithm starts on the second level and
flows the same way as before but without the ability to chose the parameters
of aggressivity level 1. If the aggressivity levels are getting less aggressive on
changing the temperature profile, the intensity of the changes diminishes over
time. This ensures that the set-point profile quickly adapts to the user at the
beginning of the process and will continue to adapt to the user afterwards, but
with a lower effect on the profile.
Besides this basic function, some other factors may be included in the decision
how the temperature set-point is changed. To ensure an adequate reaction af-
ter a “too cold” feedback for example, a minimal temperature set-point must
be chosen. Otherwise a set-point temperature of 16 ◦C, due to an estimated ab-
sence, may only be raised to 16.5 ◦C which would most likely be an unsatisfying
result for the user. User feedbacks may also be ignored if the temperature set-
point is deviating too much from the measured temperature. For example, if
the temperature set-point is 21 ◦C, but the measured temperature is only 19 ◦C,
a “too cold” feedback would be ignored, because due to the high difference be-
22
3.1 Algorithm
Start Tset(t) >= 19 °C
False
Add preheating ramp
End
Tset(t) ‐ Tset(t‐1) >= 1.5 K
Figure 3.4: Flow chart of the pre-heating algorithm. If the temperature is below19 ◦C and the temperature change is large than 1.5 K, a pre-heatingramp will be added.
tween set-point and measured temperature, the feedback cannot be used.
3.1.2 Preheating methods to improve the user experience
Most buildings in Europe rely on water based heating systems. These systems
are rather slow, especially if the walls of the building are already cooled down. If
room heating starts with the user entering the building, the cold walls will result
in a reduced global temperature and thus in a reduced thermal comfort. To
avoid this, reheating of the building starts prior to the user’s return. If heating
demand will occur in the future, the set-point temperature should already be
raised before. Two different methods were implemented to improve thermal
comfort: In the slope based pre-heating method, the algorithm increases the
temperature set-point with a slope sph. The block based pre-heating method
uses a fixed time block of increased temperature set-points prior to the user’s
expected return. For both methods pre-heating is only invoked if the set-point
is at least 19 ◦C and the step to the previous timestep is at least 1.5 K.
23
3 Methodology
Day 2 Day 4 Day 6
00:0002:0004:0006:0008:0010:0012:0014:0016:0018:0020:0022:0023:50
Day 2 Day 4 Day 616.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
Figure 3.5: Effects of the pre-heating algorithm. Left: Input temperature pro-file, right: Profile after applying the pre-heating algorithm. Only atday 2 a pre-heating ramp is added because in the other two casesthe temperature set-point is too low or the increase compared tothe previous set-point is not high enough.
Slope based pre-heating method
The slope based pre-heating method uses a value of 0.5 ◦C/h to raise the tem-
perature. Figure 3.4 shows the flow chart for the pre-heating algorithm, with
Tset being the set-point temperature, t demarking the timestep under consider-
ation and t−1 the previous timestep. Both pre-heating methods are calculated
at the beginning of a new day. The day’s inital profile is analyzed backwards,
starting from the last timestep of the day. If Tset(t ) > 19◦C, it is checked if
Tset(t )−Tset(t−1) >= 1.5. If the difference is larger a pre-heating ramp is added.
If one of the tests is false, no pre-heating ramp is added.
Figure 3.5 shows the effect of the pre-heating algorithm on the temperature
profile as carpetplot. Each timestep is one rectangle, where the x-axis shows
the date and the y-axis the time. The color of each field determines its value
24
3.1 Algorithm
with reference to the colorbar on the right side of the figure. On the left side a
generic temperature profile is shown, before the pre-heating algorithm is ap-
plied. The base temperature is at 16 ◦C, but there are three blocks with an in-
creased temperature. On day 2 the temperature block is increased to 21 ◦C, on
day 4 to 18 ◦C and on day 6 there is a temperature ramp from 18 ◦C to 21 ◦C in
1 K steps. On the right hand side the effect of the slope based pre-heating algo-
rithm is shown: A temperature-ramp in 0.5 K steps is added to the first block,
because its temperature setpoint was greater than 19 ◦C and the difference to
the previous timestep’s temperature set-point was larger than 1.5 K. The ramp
extends until the average temperature set-point of the previous six timesteps is
larger than the ramp-value. No pre-heating ramp is added to the second block
because its set-point value of 18 ◦C does not exceed the minimum temperature
of 19 ◦C. Because the temperature steps between each block are less than the
threshold of 1.5 K the third block does not get an additional ramp. The tem-
perature increases stepwise instead of continuously due to possible limitations
imposed by the hardware in use. Not every thermostatic valve draws its power
from the electrical grid but for example from batteries. A lower communica-
tion rate and less changes in the valve’s travel will thus increase the necessary
replacement cycle.
Block based pre-heating method
The second version uses a more simplified approach: If a temperature above
19 ◦C is detected and the temperature increase is more than 1.5 K, another tem-
perature block of the duration tph is added. The temperature set-point of this
block is the highest temperature set-point within the next 60 minutes of the in-
crease. The effects of this method are shown in figure 3.6.
This figure uses the same generic temperature profile as figure 3.5 with one ad-
dition, on day 3 the temperature is first increased to 20 ◦C and than to 21 ◦C.
As before, no alteration is made at day 4 and day 6 because the set-point does
25
3 Methodology
Day 2 Day 4 Day 6
00:0002:0004:0006:0008:0010:0012:0014:0016:0018:0020:0022:0023:50
Day 2 Day 4 Day 616.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
Figure 3.6: Effects of the second version of the pre-heating algorithm. Left: In-put temperature profile, right: profile after applying the pre-heatingalgorithm. Due to the same limitations as in figure 3.5 no pre-heating block is added at day 4 and day 6. At day 3 the higher tem-perature is chosen for the pre-heating block.
not exceed the threshold or the increase in the set-point temperature is below
the threshold respectively. On day 2 as well as day 3 an additional temperature
block at 21 ◦C is added. For both days 21 ◦C is used, because the maximum
temperature in the next 60 minutes after the increase is 21 ◦C.
Both methods are implemented with fixed values for sph and tph and tested by
simulations. Section 3.1.3 suggests an advanced pre-heating method where the
parameters sph and tph are dynamically calculated.
3.1.3 Learning
The previous section explained how the algorithm adapts the temperature pro-
file to the user and tries to ensure thermal comfort. The learning part of the al-
26
3.1 Algorithm
gorithm consists of two independent parts. The first part is the ability to trans-
fer user patterns from previous days to another day and successfully adapt to
the user’s demand without further feedback. The second part is learning about
the building’s thermal behavior to improve the pre-heating mechanism and
achieve better thermal comfort.
Learning about the user behavior
Besides learning the temperature profile for one day, the profile has to be ex-
tended to the following days in a reasonable manner. In this work, three dif-
ferent methods of learning were chosen. The first method is the most simple
approach: The final result of the previous day is copied to the next day, assum-
ing that the behavior will be similar each day. This approach ignores any differ-
ences between days, which will result in sub-optimal results if the days actually
differ.
The second approach is called weekly learning. Assuming that every week is
similar, the last weeks temperature profile is used to initialize the current day.
As long as there is no previous week, i. e. at the beginning of the learning pro-
cess, the daily learning method from the previous paragraph is used to learn
from the previous day, assuming that the profile from the previous day is better
than the initial profile. This method will probably be slower to learn than the
first method because it can only learn on a weekly but not daily basis. But for
adjacent days that have a different user behavior, the results should be better.
Also weekly routines (sports, regular home office) would be recognized better
by this method. For most working people a weekly schedule may be appropri-
ate, but there are situations, e. g. changing shifts, that may not show a weekly
recurrence.
The clustered learning tries to combine the advantages of both previous meth-
ods: The faster learning and the better consideration of different day profiles.
The days are clustered into groups. In this work, a static cluster is used, the
27
3 Methodology
Figure 3.7: Illustration of the different learning concepts. Top row shows thedaily learning method, the current day is always copied to the nextday. In the middle, the weekly learning algorithm copies from thecurrent day to the same weekday next week. The Cluster Learningcopies between clusters. All methods start with daily learning untilmore information are available.
weekdays are the first cluster, the weekends create the second cluster. These
clusters are defined right at the beginning of the simulation. The final result of
one day is copied to the next day of the cluster.
Figure 3.7 illustrates the different learning concepts. Some arrows in the weekly
learning part were omitted for reasons of clarity. The daily learning always
copies from the previous day, while the weekly learning first behaves the same
as the daily learning, because no previous data are available. As soon as one
week has elapsed the weekly learning starts to copy the results of one weekday
to the same weekday the next week. The clustered learning starts also the same,
copying data daily. In contrast to the weekly learning on the second Monday the
data is not copied from the previous Monday but from the previous Friday, as
those days are in the same cluster. The same is true for the Sunday data that
28
3.1 Algorithm
00:0004:0008:0012:0016:0020:00
No Learning Daily Learning
Day 1 Day 8 Day 15
00:0004:0008:0012:0016:0020:00
Weekly Learning
Day 1 Day 8 Day 15
Clustered Learning
16.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0
Figure 3.8: Influence of the different learning classes on a generic temperatureprofile. Top left shows the input temperature profile, top right showsthe effect of the daily learning. Bottom left shows the weekly learn-ing that behaves like the daily profile for the first week, but after-wards changes are only copied with a frequency of seven days. Bot-tom right shows the clustered learning that again behaves the sameas the daily learning algorithm but than copies between its differentclusters (here pre-defined as weekdays and weekends)
are copied to the next Saturday. All learning concepts do not rely on absolute
time but on relative time since start of the algorithm, but the weekly learning
algorithm needs to know the weekday. This is also true for the cluster learning
if the clusters are not calculated dynamically.
Figure 3.8 shows the effects of the different learning classes on the tempera-
ture profile. The temperature profile is generic, especially no user feedback
is considered and the temperature is always 16 ◦C except three temperature
blocks that have been manually increased to 21 ◦C on different days at differ-
ent times. The top left figure shows a temperature profile if no learning class
is applied. Because no learning method is involved, the temperature changes
29
3 Methodology
are not copied to another day. The top right figure shows the daily learning that
copies the result of the previous day to the next day. As soon as the temperature
is changed, this change is copied to each subsequent day.
The weekly learning shows a different behavior. The first temperature block at
06:00 h was introduced in the first week. Because no previous data are available
for the first week, the weekly learning shows the same behavior as the daily
learning and copies each previous day until start of the second week. Therefore
the first temperature block is present in each day of the first week, except the
first day. In the second week the previous week is copied, therefore the first
day of the week does not show increased temperature, each other day does.
The temperature block at 11:00 h was introduced on day 7 and therefore not
copied to the next day, because for the next day, a previous value is available
- the values of day 1. The same happens to the temperature block at around
16:00 h, which is introduced on day 9. These temperature blocks are repeated
once a week therefore day 14 and day 21 are the same as day 7, day 16 is the
same as day 9.
The clustered learning shows the same behavior as the daily learning for the
first temperature block. Introduced on day 2, the increased temperature is
copied to each day of the same cluster: day 3-day 5. Because no previous data
are available, for the first occurrence of the second cluster (days 6 and day 7),the
previous day 5 is copied. Day 8 is than copied from day 5, resulting in an in-
creased temperature on each subsequent day. The temperature block around
11:00 h is introduced at day 7. Because day 13 belongs to the same cluster as
day 7, the temperature block at 11:00 h is copied to this day as well as to the
days 14, 20 and 21, which are also part of this cluster. The temperature block
at 16:00 h introduced on day 9 belongs to the first cluster and is copied to each
day belonging to the first cluster, starting with day 10.
It is worth noting that feedbacks and systematic decreases were omitted, lead-
ing to temperature profiles that stay the same if not altered manually. This was
30
3.1 Algorithm
done on purpose to illustrate the differences between the learning methods. If
the adaptive algorithm is added, user feedbacks and systematic decreases will
superpose the learning algorithm and result in less similar temperature profiles.
Learning about the building's thermal behavior
In section 3.1.2 on page 23 the implementation of a pre-heating method was
suggested to improve the results of the algorithm. Both methods use fixed val-
ues for sph and tph. Given the difference of buildings it cannot be expected that
one value for the slope sph or time tph exists that will fit all buildings, but that
the value will depend on the building’s characteristics, the heating system and
variables like global radiance or outside temperature.
Dynamically changing the pre-heating parameters based on results from pre-
vious days is a reasonable approach to improve the results of the pre-heating
method. Furthermore a minimal temperature set-point avoids very long pre-
heating phases due to a building already cooled down too much.
The change in the slope sph or pre-heating duration tph, depending on the pre-
heating algorithm used, can be calculated as a sum of Heaviside functions Θ3
given k being the number of pre-heating events in one day and l the number
of successful pre-heating events. An event is successful if the measured room
temperature reaches the temperature set-point within an acceptable difference
or the measured air temperature Tsim, air is above a predefined cutoff tempera-
ture at the end of the pre-heating phase. The flow chart in figure 3.9 shows the
calculation of the pre-heat parameters sph and tph. First the ratio between suc-
cessful pre-heating events and pre-heating events of all rooms in the building is
calculated. If the ratio is below 0.7 measures are taken to increase the length of
3The Heaviside function Θ is defined as Θ(x) ={
1 for x ≥= 0
0 for x < 0. It triggers an event if a certain
threshold is reached.
31
3 Methodology
Start t > t0
l/k < 0.7
Calculate l and k
Use slopeDecrease sph according to
eqn. (3.1)
l/k < 0.85Increase tph according to
eqn. (3.2)
Use slopeIncrease sph according to
eqn. (3.1)
False False
False
Decrease tph according to
eqn. (3.2)
False
Figure 3.9: Flowchart to calculate the pre-heating parameters sph and tph. l de-notes the number of successful pre-heating events, k the total num-ber of pre-heating events. The algorithm increases the pre-heating,if the success ratio is below 0.7 and decreases the pre-heating witha success ratio above 0.85. In between these values, pre-heating re-mains constant.
the pre-heating phase, if the ratio is above 0.85 the pre-heating phase is short-
end. Between 0.7 and 0.85 no changes to the length of the pre-heating phase
are made.
32
3.1 Algorithm
For the slope based pre-heating method, the ramp’s new slope sph, new is calcu-
lated as:
sph, new = sph, old ·[
0.5+Θ(
l
k−0.25
)·0.2+Θ
(l
k−0.45
)·0.2
+Θ(
l
k−0.7
)·0.1
]+Θ
(l
k−0.85
)·0.1
(3.1)
This will decrease the slope sph for every ratio lk <= 0.7, keep sph for 0.7 < l
k <=0.85 and increase sph for every ratio l
k > 0.85. The second case uses the same
parameterization, meaning that for every ratio lk <= 0.7 the pre-heating time
tph is increased, for a ratio 0.7 < lk <= 0.85 tph kept the same and for every ratio
lk > 0.85 tph is shortened. The parameterization can be expressed as:
tph, new = tph, old +3−Θ(
l
k−0.25
)−Θ
(l
k−0.45
)−Θ
(l
k−0.7
)−2·Θ
(l
k−0.85
) (3.2)
If the success ratio lk is small, the change in sph or tph will be more pronounced
than for larger values. For tph exists a lower limit, for sph exists a lower and
upper limit, although later results show that the upper limit could be dropped.
As first simulations have shown that pre-heating phases could last more than
ten hours, an additional method was added to set a minimum temperature for
each room. This should avoid cooling down the building’s structure too much
and make it possible to reheat the rooms within a reasonable timeframe.
The flowchart of the algorithm to calculate the minimum temperature Tmin is
given in figure 3.10. First, the method queries which pre-heating method is
used. If the slope based pre-heating method is used it is checked if the current
value of the slope is already below a minimum threshold sph, min and if the suc-
cess ratio lk < 0.5. If both is true, the minimum temperature will be raised by 2 K
33
3 Methodology
Start
Use slope?[sph < sph, min
AND l/k < 0.5] OR l/k < 0.2
Tmin < 16 Tmin+ 2
Tmin+ 1sph > sph, min / 0.6 AND l/k > 0.7
[tph > tph, max AND l/k < 0.5]
OR l/k < 0.2
tph < tph, max * 0.6 AND l/k > 0.7
Tmin- 1
False FalseFalse
True
True
False
Tmin > Trejected,
warm
Tmin > Tset, max
- 2
Tmin < 10
False
False
Tmin = Trejected, warm
Tmin = Tset, max
- 2
Tmin = 10
True
True
True
Figure 3.10: Flowchart for the calculation of the minimum temperature. Tmin
will be increased if pre-heating is above a threshold and lk < 0.5
or lk < 0.2, independent of pre-heating time. Tmin is reduces, if
lk > 0.7 and pre-heating time is below a threshold. Tmin is always>= 10 ◦C, 2 K below the days maximum temperature set-point andnot above a previously rejected temperature.34
3.2 Simulation setup
if the current values of Tmin is below 16 ◦C and by 1 K otherwise. A success ratelk < 0.2 will increase Tmin independent of the current value of sph. A decrease
of Tmin by 1 K is possible, if lk > 0.7 and sph > sph, min
0.6 . The same method is used
for the block based pre-heating method, but for an increase in Tmin the cur-
rent pre-heating time tph must exceed a threshold value tph, max. After the new
minimum temperature is calculated, some more checks are made. If a mea-
sured temperature below Tmin has been rejected by a too warm feedback, Tmin
is reduced to this temperature. To avoid continuous heating of a room, it is
also ensured that Tmin is 2 K below the maximum temperature set-point of the
current day, a difference at which the valve will be closed. Finally Tmin has a
lower limit of 10 ◦Cto avoid freezing. The calculation of the minimum tempera-
ture is parameterized in a way that the minimum temperature only increases if
the necessary pre-heating phase gets too long and is not successful. So before
the minimum temperature increases, the pre-heating phase will be extended. A
once increased minimum temperature will only be decreased if the pre-heating
parameters fulfill the condition sph > sph, min
0.6 or tph < tph, max ·0.6. This parame-
terization ensures that Tmin is not lowered at the same time as the pre-heating
time gets shorter if the pre-heating phase is already long.
3.2 Simulation setup
The suggested algorithm has been tested in two different ways: In a field test
and by simulations. In the field test the algorithm was deployed in real build-
ings with users, simulations were used to test and improve the algorithm. To
test the algorithm using simulations, it is necessary to provide a simulation for
the heating system and thermal environment. This was achieved with Modelica
models. Furthermore, a behavioral user model is needed that has a non-static
occupancy schedule and the ability to rate the thermal environment. This user
model was setup in Python. The user feedback was used as input to the algo-
rithm, the results of the algorithm were fed into the Modelica simulation. This
35
3 Methodology
Figure 3.11: Graphical overview about the different parts of the learning algo-rithm and the simulation test environment. Calculations are con-ducted in Python (grey boxes), simulations are conducted in Mod-elica (red). Each user can have a different behavioral model andeach room has its own presence profile. Arrows indicate informa-tion flow between the modules.
would than have an effect on the user’s thermal comfort. The necessary parts
to gather results are described in the following sections. Figure 3.11 gives an
overview of those parts and how they interact.
A central Python structure controls data exchange between simulation and al-
gorithm. The presence profile for each user gives the user characteristics in-
formation about the user’s current location. Combined with the temperature
36
3.2 Simulation setup
information from the thermal environment simulation the behavioral model
can evaluate the thermal comfort of each user in his current location. The cu-
mulated feedback of all users in one room is passed to the learning algorithm
for each room. The learning algorithm decides which set-points must be ad-
justed and the changes are stored in the set-point information class for each
room. This class exchanges the current set-point for each room with the ther-
mal environment simulation. The object-oriented approach with clearly de-
fined interfaces allows replacement of one object without the need to change
different objects. For example, the change in thermal comfort evaluation (see
section 3.2.2) is realized by replacing the User Characteristics class with another
class.
The coupling of user and algorithm with the thermal environment is shown in
figure 3.12. A simulation is started to simulate the building for one timestep of
10 minutes. After these 10 minutes the presence profile checks if any room in
the apartment was occupied. If this is true, the average temperature for this
room is calculated from the simulations result file. The average air and sur-
face temperature are passed into the behavioral model of the user. Depending
on the user’s feedback the algorithm adapts the temperature set-points for this
room. If the user is not present, no feedback is given, which may result in a sys-
tematic decrease triggered by the algorithm. With these altered set-points the
simulation continues for the next 10 minutes. At the end of the day one of the
learning methods described in section 3.1.3 is triggered to advance the relevant
set-point changes to the next day.
The presence profile is introduced in section 3.2.1, the user characteristics are
explained in section 3.2.2, a brief description of the thermal environment is
given in section 3.2.3. The functionality of the algorithm has been explained
in detail in section 3.1
The set-point information class is a storage class and therefore not described in
detail. It is used to keep track of the changes made by the algorithm and keeps
37
3 Methodology
Start
Run Simulation for 10 Minutes
Algorithm determines new
setpoint
Room was occupied
True
Send „No Feed-back“ information to the algorithm
False
Reached stoptime?
End
Get room temp-eratures from the
simulationTrue
Determine user Feedback
Get occupancy information for
each room
True
False
Reached end of day?
False
TrueStart learning
methods
Figure 3.12: Flow chart for the interaction between simulation and algorithm.Modelica simulates 10 minutes, than occupancy information arecollected for each room. If no user was present, no feedback isgiven to the algorithm. Otherwise, the thermal environment is an-alyzed and the algorithm decides if the user gives feedback. Thenew set-point is calculated and advanced to the simulation. If theend of one day is reached, the learning method is triggered.
38
3.2 Simulation setup
all relevant data for later analysis. There is one instance of the set-point infor-
mation class for each room in the simulation that stores the current set-point,
the user’s feedback, measures taken by the algorithm and also the simulated
temperature values4.
3.2.1 User Presence
A key requirement for any interaction between user and building is the user’s
presence. Simple patterns that repeat themselves daily (so called static pro-
files) fail to represent realistic user behavior. To improve on these static profiles,
Wang et al. [2005] suggested the use of two different exponential distributions
to model occupancy and vacancy in an office, Page et al. [2008] suggested us-
ing Markov chains to model occupancy or vacancy for an office or residential
building. Richardson et al. [2008] used Markov chains to predict the number of
active users present in a residential building. He used data from a time-use sur-
vey (TUS) to create the necessary transition matrices. A similar approach was
made by Widén et al. [2009]. He used a three-state non-homogeneous Markov
chain with the transition probabilities also derived from a TUS. Liao et al. [2012]
extended the model suggested by Page et al. [2008] to a model with an arbitrary
number of zones and occupants. The results were tested against a commer-
cial building. Especially in conjunction with several zones the input data are
complex and simplifications yield wrong results.
A drawback to all (except Liao’s) presented methods is the use of only two or
at maximum three states (absent, present, present but inactive). As the user’s
input is used for a single room control system, a global presence information
is not sufficient. Based on the four levels of details suggested by Feng et al.
[2015, p. 349], this work needs the most detailed level of a presence profile with
4The simulated data could have been re-read from the simulation output files but for efficiencyand speed these data were also stored in the temperature profile. For an advanced in-depthanalysis all simulated data are stored in addition to the set-point information class.
39
3 Methodology
individual tracking on the occupants level. Data on this are scarce, especially
for residential buildings.
Data about user behavior are typically collected by use of a TUS or a field test
(FT). Both methods have their advantages and drawbacks: TUSs are highly de-
tailed activity logs for individual people, allowing the necessary tracking. But
they usually cover only a short period of time (mostly 24 hours) and therefore
do not allow conclusions about routine behavior of the occupant. Although the
data are logged on a personal level, the choice and number of people partici-
pating in the TUS should ensure that the aggregated data reflect a population
sample. Data from FTs normally cover less people than TUS data, which may
often result in a sample that does not reflect the whole population, especially
as many FTs on occupancy were conducted in office buildings or universities.
Nonetheless, FTs run for a longer time and give the ability to analyze routine
behavior of people. Due to the sensors used to detect occupancy (movement
sensors, CO2 etc.) they can often only detect if the space is occupied but not by
whom, missing the necessary individual level.
The facts that most occupancy models are not as detailed as necessary for this
work and that the input data are scarce and often not detailed enough, led to
the decision to create a simple tool that uses generic presence probabilities to
create dynamic occupancy profiles for residential buildings down to the resolu-
tion of single rooms. These dynamic profiles share a probability distribution so
they will be similar but not the same, which is a better representation of human
behavior than a static profile. A difference between weekdays and the week-
end seems realistic to reflect different user behavior on these days. Figure 3.13
gives an overview how the tool works. This approach ensures that the profile
is derived from a reduced set of input data and easily to compute. Creating a
universal tool to compute validated presence profiles was not the scope of this
tool. For this, other approaches would be more suitable. Aim of this tool was
the creation of a reasonable user presence profile that is capable of testing the
algorithm.
40
3.2 Simulation setup
Start
Retrieve app‐licable presence probabilty distri‐
bution for timestep
Use random number to
decide user’s position
Use random number to get duration from room specific distribution
Use persistency method?
TrueRecalculate presence
probabilites
False
Fill timesteps according to duration
Reached stoptime?
False
True
Ensure Bathroom Usage?
False
End
True
Include bathroom usage in the morning and evening
Figure 3.13: Flow chart to create the occupancy profile for one user. The appli-cable presences probability distribution for the current timestepis querried and may be recalculated if the persistency method isused. A random number is used to decide the users position, an-other random number decides about the length of the presence.
41
3 Methodology
The algorithm starts by retrieving the applicable presence probability distribu-
tion for the current timestep. These probability distributions contain a proba-
bility 0 < pi (t ) < 1 for every room i of n rooms at time t with∑n
i pi (t )+pa(t ) = 1
for every timestep t and pa(t ) the probability of absence at timestep t . The re-
trival of the presence probabilities is followed by the decision if the persistency
method should be used. The persistency method allows for a persistency pa-
rameter π that increases the probability that the user will be in the same room
as the day before at the same timestep. The persistency factor will especially
effect timesteps where many possible locations have a similar possibility. To
avoid the manifestation of unlikely events, the persistency factorπ is countered
by a minimum value pi,min for the probability presence pi (t ) that the persis-
tency factor is used. If the persistency model is used, the previously retrieved
presence probabilities must be recalculated, otherwise they will be kept unal-
tered.
After finally retrieving the applicable presence probabilities, a random num-
ber is drawn to decide the current position of the user. In a next step, another
random number is drawn to determine the duration how long the user occu-
pies the current location. To estimate the duration the random number is com-
pared to a normal distribution. The normal distributions may have a different
parameterization for mean and standard deviation for each room. According
to the duration the adjacent timesteps are filled with the location. This pro-
cess will continue until a predefined stoptime is reached. After the stop time
is reached, there is the option to alter the created occupancy profile, to ensure
that the user visits the bathroom in the mornings and evenings. This is assumed
to be a typical behavior for most people. The occupancy profiles in this work
used the persistency method and the method to ensure bathroom usage.
Figure 3.14 shows the generated occupancy profile as a discrete carpetplot. The
different possible locations are color-coded for each timestep. The different
days are shown on the x-axis, the y-axis shows the different times of the day.
The occupancy profile is six weeks long and each day has 144 timesteps, there-
42
3.2 Simulation setup
Day 1 Day 15 Day 29 Day 43
Date
00:0002:0004:0006:0008:0010:0012:0014:0016:0018:0020:0022:00
Tim
e
Bedroom
Bath
Kitchen
Living Room
Study
absent
Figure 3.14: Occupancy profile used as input for the simulated user behavior.Different colors indicate different rooms. Between midnight and06:00 in the morning the user is usally asleep. During weekdaysthe user is not present at daytime and spends most if his eveningsin the living room. At the weekend the user is more often at homeduring the day.
fore the figure shows 144 rows and 42 columns. The two different probability
distributions that were used to create the occupancy profiles for the weekdays
and weekends are clearly distinguishable. On a normal weekday the user is usu-
ally not at home between 09:00 h and 18:30 h, sleeps between 23:30 h and 06:30
and spends most of the evening in the living room. On a weekend, the user
stays in bed longer and is more often at home during the day.
The generated profile fits the expectations concerning a dynamic profile: It
shows similarity between similar days (weekday, weekend), but also variety.
Therefore this presence profile is considered as a reasonable input to simulate
the user’s presence. The similarities between similar days allows learning algo-
rithms to detect patterns. But in contrast to always repeating static profiles, the
algorithm will be confronted with deviations from the expected pattern. Total
43
3 Methodology
duration of the profile is 1008 hours, of these the user is asleep for 319 h and
away for 373 h.
3.2.2 User Behavior
A user presence model was developed in the previous section to be able to test
the algorithm with simulations. This model evaluates if the user is present
and thus able to give feedback. Additionally, a behavioral model for the user
is needed to evaluate if the user will give feedback depending on the current
state of his thermal surroundings. Due to the simple input of the algorithm, the
behavioral model must only be able to rate the thermal comfort and decide if
the user takes action in form of a “‘too cold” or “too warm” feedback.
To decide between different behavioral models following conditions were con-
sidered:
. The thermal environment is well known, as we use simulations and are
not limited to measured sensor data. Thus, a complex model with regards
to environmental input may be used to evaluate the user behavior.
. In contrast to the environment, the knowledge of user behavior in resi-
dential buildings is limited. The implemented method should be as sparse
as possible on user specific information like clothing or activity levels.
. The development of a behavioral model is not the scope of this work, a
well accepted, easy to use model is thus preferred to a highly experimen-
tal one.
Considering the above, two different methods are used in this work. One is the
model of the predicted mean vote (PMV) and predicted percentage of dissatis-
fied (PPD), suggested by Fanger [1970]. Fanger’s model needs extensive input
on environmental and user behavior, but is well accepted. The other model
suggested by Daum et al. [2011] is a newer model. It needs only air temperature
as input but is far less tested and accepted compared to the Fanger model.
44
3.2 Simulation setup
Thermal comfort evaluation based on Fanger's PPD method
The Predicted Mean Vote (PMV) and the thereof derived Predicted Percentage
of Dissatisfied (PPD) was published by Fanger [1970] and is an often used ther-
mal comfort model. For example DIN EN ISO 7730 [2006] and ASHRAE [2013]
use the PMV and PPD method to predict thermal comfort. The PMV depends
on air and surface temperatures, relative humidity and air speed. Furthermore,
the clothing level in clo and the metabolic rate in met is considered. The tem-
perature data can be taken from the simulation, but values for air velocity and
relative humidity are not supplied by the simulation. Therefore constant values
of 0.1 m/s and 50 % were used for air velocity and relative humidity respectively.
The user dependent values for clo and met are not given as well. Assuming that
a person sticks to a clothing level but uses a blanket when sleeping, constant
values for clo and met were chosen. The values used throughout all simulations
are given in table 3.1. Given the fixed values for relative humidity, air-speed,
clothing factor and metabolic rate, the highest comfort is achieved at 21 ◦C if
the user is awake and 18 ◦C if the user is asleep.
These data are used to calculate the PMV. The PMV has a range from -3 to 3. The
optimal value for the PMV is zero, negative PMV values indicate the thermal
sensation being too cold, positive values indicate the thermal sensation as too
warm. The PMV is linked to the PPD by equation (3.3):
PPD = 100−95−e−0.03353 ·PMV4−0.2179 ·PMV2(3.3)
Given equation (3.3), a PMV equal to zero equates to a PPD of 5, indicating that
5 % of a group of people would be dissatisfied with the thermal comfort. At a
value of PMV =±1.5, the PPD is 50, indicating that 50 % of the people would be
dissatisfied with the current thermal environment.
Figure 3.15 gives an overview how the model determines the user behavior within
the simulation. First air and surface temperatures are retrieved from simula-
45
3 Methodology
Start
Simulated Data
User Parameters
Calculate PMV and PPD
PMV > threshold AND
r < PPD
Feedback = warm
True
Parameter Data
PMV < threshold AND
r < PPDTrue Feedback = cool
FalseFeedback =
None
Comf = PMV
False
Figure 3.15: Flow chart to rate thermal comfort and estimate the user actionbased on Fangers PMV and PPD method. Depdending on sim-ulated and user data, PMV and PPD are calculated. The PMV isstored as comfort information. The values of PMV and PPD decideif the user will give feedback.
tions, user parameters for clothing and metabolic rate are chosen based on the
data given in table 3.1 and fixed values for air velocity and relative humidity
are used to calculate the PMV. The PMV is stored as a comfort value for further
use. Then the PPD is calculated based on the PMV and a random number r is
drawn. If the value of the PMV is larger than a predefined comfort threshold and
r is smaller than the PPD, the feedback “too warm” is given. If the PMV values
is smaller than the comfort threshold and the random number is also smaller
than the PPD, the feedback “too cold” is given. In any other case, no feedback
46
3.2 Simulation setup
is given. In this work the comfort threshold is set to 0.55 , so in a PMV range
of −0.5 < PMV < 0.5 no feedback is given. PMV values outside this range may
result in a feedback, depending on the PPD and a random number.
Thermal comfort evaluation based on Daum's model
While Fanger used data from many persons tested in a climate chamber and
afterwards averaged those data, Daum measured the comfort of six individu-
als and used a multinominal logistic model to create probability distributions
for comfort votes for each individual. He uses data gathered between 2006 and
2009 at the LESO university office building [Daum et al., 2011], for a detailed
description of the survey, see Haldi and Robinson [2010]. Due to the experi-
mental setup the results are only valid for normally dressed and awake persons
and cannot be applied to a sleeping person under a blanket.
In this model S labels the thermal sensation of the user, S−1 indicates that the
user feels too cold, S1 a too warm sensation and S0 a good one. For every possi-
ble thermal sensation, the probability p(Si |Tin) for a given sensation Si , is only
dependent on the indoor temperature Tin and given by the equation:
p(Si ) = exp(ai +bi Tin)∑1j=−1 exp(a j +b j Ti n)
, (3.4)
5This comfort threshold represents category B for a thermal environment in accordance to DINEN ISO 7730 [2006].
Table 3.1: Values for metabolic rate and clothing factor in dependence of user’sstate
Metabolic Rate in met Clothing Factor in clo
User awake 1.1 1.2
User asleep 0.8 3.1
47
3 Methodology
0.00.20.40.60.81.0
Subject 1 Subject 2
0.00.20.40.60.81.0
Pro
bab
ilit
yfo
rfe
elin
g...
Subject 3 Subject 4
15 20 25 30 35
Indoor Temperature in ◦C
0.00.20.40.60.81.0
Subject 5
15 20 25 30 35
Indoor Temperature in ◦C
Subject 6
Too Cool Comfortable Too Warm
Figure 3.16: Probability distribution to feel cold, warm or comfortable for sixsubjects, based on data by Daum et al. [2011]. Not only preferredtemperatures (peak of the grey line), but also accepted tempera-tures vary between the subjects in a wide range. 5 out of 6 subjectprefer temperatures above 20 ◦C.
with a0 = b0 = 0. The values of the other parameters for each of the six individ-
uals can be found in table 3.2, the probability distribution plots for each person
are depicted in figure 3.16.
Depending on the indoor temperature Tin, each user has three probability curves
48
3.2 Simulation setup
to rate the environment as “too cold” (blue line), “comfortable” (gray line) or
“too warm” (red line). Low room temperatures < 18 ◦C are considered as too
cold, high temperatures > 27 ◦C are mostly considered as too warm. The com-
fortable temperatures are in between those values, for most users in the range
between 22 ◦C and 25 ◦C, the only exception is person 6 with a preferred tem-
perature of 20 ◦C. The tolerance towards deviations from the preferred temper-
ature also vary. Person 6 is rather intolerant against deviations from his pre-
ferred temperature, while person 5 is rather tolerant with respect to cooler tem-
peratures, but less tolerant to high temperatures. Some people even show am-
biguity so that a temperature can be rated “too cool” as well as “too warm”.
Figure 3.17 shows how these data are used to derive the user feedback. First the
algorithm determines if the user is asleep. If the user is asleep, thermal comfort
is rated by Fanger’s method as a fallback method.6. That the sleeping phase is
evaluated differently is not critical due to the special consideration of the bed-
room (see section 3.2.5). If the user is not asleep, the air temperature and the
individual user parameters (see table 3.2) are used to calculate the probabili-
ties p(S0), p(S−1) and p(S1) as defined in equation (3.16). If p(S0) is larger than
p(S1) and p(S−1), the comfort is rated as good, if p(S−1) has the highest value,
6In Daum’s method the influence of the clothing factor is implicitly included in the fitting param-eters. But as it was a study conducted in an office, the case of a sleeping person using a blanketwas not observed.
Table 3.2: Fitting Parameters for Daum’s thermal comfort model according toDaum et al. [2011]
Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6
a−1 14.50 10.41 12.20 39.36 12.34 18.42
b−1 −0.71 −0.48 −0.58 −1.43 −0.66 −1.09
a1 −44.56 −14.12 −25.36 −36.73 −39.72 −30.74
b1 1.77 0.51 0.95 1.46 1.38 1.32
49
3 Methodology
the comfort is rated as too cold and if p(S1) is higher than the other values, the
comfort is rated as too warm. A low comfort rating must not lead to a user feed-
back in every case.
To decide if the user takes action two random numbers r1 and r2 are drawn.
If feeling cold is more likely than feeling warm, p(S−1) is compared to the first
random number and if r1 < p(S−1), a “too cold” feedback is triggered. If no feed-
back is triggered, p(S1) is tested against r2, and if r2 < p(S1), a “too warm” feed-
back is triggered. If both cases fail, no feedback is triggered. If p(S1) > p(S−1),
it is first tested if the user will give a “too warm” feedback and afterwards a “too
cold” feedback to give precedence to the case that is more likely. Within this
work the Daum user is initialized with the parameterset of subject 1 (see ta-
ble 3.2) resulting in a preferred temperature of 23 ◦C, slightly higher compared
to the PPD-user.
Bene�ts and Drawbacks of both methods
This section compares the previously introduced behavioral models of Fanger
and Daum and focuses on their advantages and disadvantages. A general draw-
back of both methods is that they are only capable of judging thermal comfort
but they cannot predict if the user will take action due to the thermal environ-
ment. At least Daum et al. [2011] successfully showed that the distributions
shown in figure 3.16 can be recreated from a simple “too cold”-“too warm” feed-
back method. A user may not take action, although he is not comfortable due
to other obstacles. He may be too focused on another task or his discomfort is
not so severe that he would bother to get up and turn the thermostat. These
external factors cannot be reproduced by the previously described models.
Especially the Fanger model, being recognized for decades and extensively used
for predicting thermal comfort, was thoroughly researched and critizied. Van
Hoof [2008] gives an extensive overview about the perceived drawbacks of this
model. The most important one is that thermal neutrality, which is captured
50
3.2 Simulation setup
Start
Air temp-erature
User Parameters
Calculate Probabilities:
p(S-1), p(S1), p(S0)
p(S0)> p(S1)AND
p(S0)> p(S-1)Comf = GoodTrue
False
p(S0)< p(S-1) AND
p(S1)<=p(S-1)True Comf = Cool
False
p(S0)< p(S1)AND
p(S-1) <= p(S1)True Comf = Warm
User asleep?
False
TrueUse Fanger
Method
p(S-1) > p(S1)r1, r2 True p(S-1)> r1 TrueFeedback =
cool
p(S1)> r2 TrueFeedback =
warm
False
Feedback = None
False
p(S1)> r1
False
p(S-1) > r2
False
FalseFalse
Figure 3.17: Flow chart to rate thermal comfort and estimate the user actionbased on Daum’s comfort measuring method. Instead of the PPDand PMV, the probabilities p(S1), p(S0) p(S−1) are used to estimateuser feedback and thermal comfort.
51
3 Methodology
with Fanger’s psycho-physical model, is not necessarily a measure of thermal
comfort. The relationship between thermal comfort and thermal neutrality
may even be an asymmetrical one and dependent on the season. Values outside
of the three central categories of the ASHRAE 7-point scale of thermal sensation
may not necessarily indicate thermal discomfort [van Hoof, 2008, pp. 186, 188].
Furthermore the PMV was never constructed as an estimate for an “average”
person but as an estimate for a large group of people, making the application
of the PMV model to one person questionable. Fanger’s model was also meant
to be applied to air conditioned buildings only and is not applicable to natu-
ral ventilated ones [van Hoof, 2008, p. 184, p. 188]. Depending on the input
variables (temperature, relative humidity, metabolic rate, clothing factor and
air velocity), Humphreys and Fergus Nicol [2002, p. 671-673] showed that the
PMV estimates are only valid under severely restricted conditions.
With respect to this work, two constraints of the Fanger model are of impor-
tance. The PMV is only applicable for steady state conditions. [van Hoof, 2008,
p. 194]. The algorithm lowers and increases the room temperatures, result-
ing in transient conditions. Nonetheless, it seems reasonable to expect that a
temperature gradient in the desired direction may improve the thermal com-
fort thus making the PMV a worst case scenario for transient conditions. Ad-
ditionally, other researches found different correlations between PMV and PPD
(figure 3.18), all of them resulting in a higher percentage of dissatisfied, the only
exception being Yoon et al. [1999] at PMV values < -0.8. The PPD curve is used
to impose a feedback method therefore the chances for a feedback will differ
with different PMV-PPD correlations. The differences in the PMV-PPD corre-
lation has several reasons. Fanger used a climate chamber while other studies
were conducted in naturally ventilated buildings. Fangers test persons were
mainly from Northern Europe, other studies were conducted in different loca-
tions across the world. Also small sample sizes may impact the representative-
ness of the studies.
Applying the Fanger model seems nonetheless reasonable, not only because it
52
3.2 Simulation setup
−3 −2 −1 0 1 2 3
PMV
0
20
40
60
80
100
PP
D
Fanger
Araújo1999
Yoon 1999
Mayer1997
dePaula2000
Figure 3.18: Alternative relationships between PMV and PPD found by Araújoand Araújo [1999]; Yoon et al. [1999]; Mayer [1997]; de Paula Xavier,Antonio Augusto and Lamberts [2000].
is still used in several standards to judge thermal comfort. Although improve-
ments and modifications have been suggested, for example an adaptive com-
fort model, they have not yet found the same widespread application in envi-
ronmental engineering practice [van Hoof, 2008, p. 198]. With respect to this
work, a special benefit of the Fanger model is the ability to rate the thermal
comfort easily and to decide, if the thermal comfort level is acceptable. For ex-
ample DIN EN ISO 7730 [2006] gives different examples to categorize the quality
of the thermal environment by the PMV.
In contrast to Fanger’s model, Daum’s model measured thermal comfort in-
stead of thermal neutrality and it is a measure for individuals and not for a
large group of people. As a drawback it is not accepted in any standards and
of far less support. Furthermore, rating thermal comfort cannot be stored as
a number, as the PMV in Fanger’s model, but rather as an acceptable or unac-
ceptable condition (see figure 3.17 for the implementation of thermal comfort
53
3 Methodology
rating). None of the two methods take into account that it is more likely for a
user to adapt to his environment within the first few minutes after arrival or the
last few minutes before departure [Gunay et al., 2013, p. 35].
In this work the user behavior is only simulated to decide if there will be input
to the adaptive system or not. Therefore, many of the restrictions in the comfort
estimations are of minor importance. Although both methods make no predic-
tions if the user will take action due to the thermal discomfort, it was necessary
to implement a method. For obvious reasons, the probability to give feedback is
non-linear, increasing the more the temperature differs from the user’s comfort
zone. This property is captured by both methods that predict user feedback for
the behavioral models: Neither the PPD curve nor the probability distributions
of Daum’s model are linear.
Given the fact, that “it is impossible to exactly predict thermal comfort for in-
dividuals” [van Hoof, 2008], any implementation of user behavior would be up
for discussion. The models could gain additional complexity, for example by
correlating the clothing level with the day ahead outdoor temperature as sug-
gested by Haldi and Robinson [2011, p. 681], but within the scope of this work
it was decided to keep the user behavior simple, especially as these models can
only partially predict the variances in clothing levels [Gunay et al., 2013, p. 39].
While the use of Fanger’s model is mainly justified by its widespread adoption,
Daum’s model is used because of its individual based data and that no further
assumption about the users behavior needs to be made. For the rest of this
work a behavioral model of the user based on Fanger’s work will be referred to
as PPD user, emphasizing that the feedback is dependent on the PPD value. The
behavioral model according to Daum will be referred to as Daum user.
3.2.3 Simulating the thermal environment
The previous two sections focused on the way the user is simulated, this sec-
tions introduces the model that simulates the thermal environment. The sim-
54
3.2 Simulation setup
ulation of the thermal environment is necessary to test the algorithm within
a software framework. The Modelica language is used as programming lan-
guage and existing models from the AixLib [Müller et al., 2016] are used to
build the model. The apartment with its hydraulic system was simulated us-
ing the OneAppartment_Radiators model from the BuildingAndEnergySystem
package within the Building.HighOrder.House.MFD package of AixLib. This
model consists of five heated rooms and one unheated corridor with a floor
space of approximately 69 m2. Each room has a one-dimensional air node that
is in convective thermal exchange with interior and exterior walls, windows and
the radiator. Each wall is in radiative exchange with the other walls and the radi-
ator. The interior walls are thermally connected to the adjacent rooms and ex-
terior walls are connected to the ambient environment. The interior walls to the
adjacent apartments behave adiabatic. The ambient data, e. g. outside temper-
ature, solar radiation and wind speed are taken from the Test Reference Years
2010 (TRY) provided by Deutscher Wetter Dienst for region 12 [DWD, 2010]. A
fixed air exchange rate (AER) models the air exchange in conjunction with an
infiltration rate according to DIN 12381. A schematic of the flat is shown in fig-
ure 3.19.
The rooms are heated with standard radiators, using water as heating fluid. A
simple boiler is used as heat source, the flow temperature is dependent on the
current outside temperature. Each room has one radiator controlled by a ther-
mostatic valve, which controls the mass flow and thus the energy delivered into
the room. The piping network replicates a hydraulic system but is ideally insu-
lated and does not exchange energy with its surroundings.
To get insights on the influence of the total energy demand of the building on
possible savings of the single room control algorithm, three different defini-
tions for the insulation of the exterior walls and air exchange rates were used.
Table 3.3 gives an overview about the values in use. Differentiating the insula-
tion standard by keeping the heating system the same will show some insights
how the savings are related to the thermal comfort and the systems energy de-
55
3 Methodology
Livingroom
Sleepingroom
Corridor
Bath Kitchen
Studyheat transfer
no heat transfer
adjacent flat
outside
outside
solar radiation
air exchange
Figure 3.19: Layout of the apartment and boundary conditions. Heat exchangetakes place between the different rooms of the apartment and theoutside. The walls to adjacent flats are adiabatic.
mand. The high energy demand setup uses a heating system with increased
power. An increased flow temperature allowed for this higher power output.
Initial tests have shown that without this increased power the heating system
was not able to supply the default temperature set-point of 21 ◦C.
3.2.4 Using an alternative heating system
The standard simulation uses a water-based radiator heating system because
this is a very common heating system, but especially for intermittent heating
it is not the best possible system. Due to its rather small area compared to the
complete surface area of the walls, rather high temperatures must be used to
heat the room. Also reheating the wall’s surface is rather slow. At least for the
PPD user thermal comfort is dependent not only on air but also on surface tem-
56
3.2 Simulation setup
Table 3.3: Different parameterization of the three different thermal environ-ments, the labels low, medium and high refer to the energy consump-tion of these setups.
Low Medium High
AER [1/h] (Kitchen and Bath) 1.0 1.2 1.3
AER [1/h] (Remaining Rooms) 0.5 0.7 0.8
Insulation quality increased standard lowered
Heating System Power standard standard increased
perature7. A better suited system for intermittent heating would be a radiant
panel heating system. Therefore the algorithm is also tested with an radiant
heating system, modeled as fast reacting heater with limited power output: A
PI controller measures the difference between temperature set-point and simu-
lated temperatures. Depending on this difference, heat is supplied to the room.
This setup can increase the heat output into the room by several 100 W within a
few minutes. 70 % of the heat is supplied through the radiative connector, 30 %
through the convective connector. A 70 % ratio for radiative heating is a typi-
cal value for wall mounted panel heating. Increasing the radiative ratio helps
to increase the overall surface temperature. This is necessary because heating
the air is not the primary problem for the current heating system: Given an ap-
proximate room volume of 50 m3, it would take just 60 kJ to reheat the air of the
living room, assuming a density of ρair = 1,2kg/m3 and a specific heat capacity
of cp, air = 1kJ/(kg·K). With the standard heaters used in this simulation, the
energy can be supplied in approximately one minute to every room, assuming
no thermal inertia, no thermal losses and optimal heat transfer between heater
and air. The maximum power of the alternative heating system is set to simi-
lar values compared to the nominal value of each corresponding heater in the
7By design the Daum user does not rely on surface temperatures.
57
3 Methodology
Table 3.4: Nominal power of the heating system for the standard and fast radia-tive heater system.
Nominal/Maximum Power in W
Room Standard Heating System Fast, radiative HeaterSystem
Living Room 1276 1300
Bedroom & Study 882 900
Bathroom 603 600
Kitchen 576 600
standard heating system, detailed data are given in table 3.4. Due to its low la-
tency and high radiative ratio this heating system will be referred to as “fast,
radiative heater” throughout this work.
3.2.5 Evaluation Methods
This section discusses how the data gathered from the simulations can be eval-
uated. As discussed in chapter 1, the main scope of this project is to reduce the
energy demand by simultaneously keeping the thermal comfort of the users.
In winter, thermal comfort in a building can only be sustained if one keeps the
building from thermal equilibrium with the outside. This requires energy and
therefore thermal comfort and energy demand are usually two opposed goals.
To be able to judge the adaptive algorithm, two different reference cases were
used, one comfort focused and one focused on saving energy.
The Reference Cases
The first reference case uses a two value temperature profile for each room ex-
cept the bedroom: 21 ◦C at daytime between 06:00 h and 23:00 h and 18 ◦C at
58
3.2 Simulation setup
night. The bedroom is constantly set to 18 ◦C. This reference case is designed to
show ideal results for the PPD user while the Daum user’s experience is slightly
on the cold side, but still rather comfortable than too cold (for reference of the
Daum user’s prefered temperatures refer to figure 3.16). Due to the constant
heating over the day, this case will not yield the best possible results with re-
gards to energy consumption. In a real world scenario this behavior represents
a user who does not use his TRV within the day but uses a night setback, a rather
realistic real world scenario [Karjalainen, 2009; Kempton and Krabacher, 1984].
The temperature profile is independent from the user’s occupancy profile. Fig-
ure 3.20 shows this temperature profile on the left side.
The second profile uses a room temperature of 16 ◦C throughout the day and
night. The temperature is only increased to 21 ◦C if the user occupies a room
except the bedroom. This use case will ensure a rather low energy demand but
due to the thermal inertia of the building one must expect a lower thermal sat-
isfaction compared to the first use case. In a real world scenario this would rep-
resent a user who increases the room temperature on entry and decreases the
temperature when leaving. The temperature profile is closely correlated with
the occupancy profile. The resulting temperature profile is given in figure 3.20
on the right side. Originally, the efficient user’s setback temperature was also
18 ◦C as compared to the finally used 16 ◦C. But this parameterization led to a
much lower thermal comfort than in the standard case but did not reduce the
energy demand as much as expected. With a setback temperature of 16 ◦C the
energy consumption was lowered by 20 % compared to the standard behavior
with only a minor further impact onto thermal comfort.
Throughout this work,the first behavioral model is referred to as “standard” be-
havior, abbreviated as “S” in labels, the second model is referred to as “efficient”
behavior, abbreviated as “E” in figures8.
The two scenarios show the two extremes between which the algorithm can
8See section 3.2.6 for more information about the labeling used throughout this work
59
3 Methodology
Bath
Bedroom
Kitchen
Living Room
Study
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
Day 1 Day 15 Day 29
RSM
00:00
08:00
16:00
23:50Day 1 Day 15 Day 29
REM
16.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
20.5
21.0T in ◦C
Set Temperature Profiles
Figure 3.20: Temperature set-point profiles for the two reference cases. Thestandard user behavior is independent of the user’s presence,the efficient user behavior correlates completely with the user’spresence.60
3.2 Simulation setup
work. The standard behavior is optimized for comfort without any regard to
energy consumption while the efficient behavior is optimized for energy effi-
ciency with low consideration of thermal comfort. The algorithm is likely to
find solutions in between these two extremes. The standard behavior is by de-
sign optimal for the PPD user and still very good for the Daum user. The effi-
cient behavior shows a low energy consumption with low thermal comfort, a
result far from optimal for intermittent heating. The algorithm’s intermittent
heating should provide better thermal comfort.
Categories used for Evaluation
The first benchmark for the algorithm is the energy consumption which can
be compared by summing up the energy flow through the radiators. The total
energy consumption is than given by equation (3.5).
Qtotal =n∑
i=0
∫ tend
t=0Q̇i ,conv(t )+Q̇i ,rad(t )dt (3.5)
with Qtotal9 being the total energy consumption of the apartment, n the number
of rooms in the flat, Q̇i ,conv the convective power or the radiator in room i and
Q̇i ,rad the radiative power and t indicating the current timestep.
Rating the thermal comfort is more complex because the two different imple-
mentations of user behavior (see section 3.2.2 on page 44) are not directly com-
parable. Also the Daum user prefers slightly higher room temperatures than the
PPD user. For both user implementations comfort was measured by the time
a comfort threshold was not met. For the PPD user the threshold was ±0.5 for
the PMV. If the PMV was above this threshold, this timestep was determined
as not meeting the criteria. For the Daum user every timestep was counted as
not meeting the criteria, if the probability of a dissatisfaction rating was above
9As mentioned in section 3.2.3, losses, for example of the piping network, are not simulated.
61
3 Methodology
0.5. This value is not comparable between the two users models, but it is com-
parable between different simulations using the same behavioral model. Not
counting slight deviations from the optimal temperature as thermal discom-
fort is justified, as literature shows that these are normally acceptable whereas
larger deviations are not.
Besides the two main criteria energy demand and comfort two additional cri-
teria can be considered: The first criteria is the necessary effort the user must
make to achieve the desired results. In the reference cases, the standard behav-
ior has a rather low effort, because the night setback may take place automati-
cally by a central controller, otherwise it will need eight daily interactions with
the heating system (raising and lowering the temperature in four rooms).
In contrast the efficient behavior is rather demanding. Each time the user en-
ters or leaves the room, he must interact with the heating system. The effort
for the adaptive cases using the algorithm can be measured as the number of
feedbacks given to the system and should be below the necessary feedbacks of
the efficient behavior reference case.
The second criteria is the usability of the system. As motivated above, more
advanced systems often result in systems that are too complex to be used by the
majority of the users. As we use a simulated user, there is no data available to
rate this but the proposed method can be compared with the findings outlined
in section 2.3 and the results from the field test.
Special consideration of the bedroom
The bedroom has a lower set-point temperature than most of the other rooms,
therefore heating is less often necessary. Due to the higher clothing factor used
if the user is asleep, the user may often be comfortable with an unheated bed-
room. This combination results normally in rather high thermal comfort, al-
though the algorithm has not really an effect as the room is not heated. There-
62
3.2 Simulation setup
fore the room is left out of consideration in several of the following analyses, or
two values are shown; with and without consideration of the bedroom to show
the differences. If the bedroom is considered or not is clearly stated when de-
scribing the results.
Due to the special use case of the bedroom it may be questioned in general if
the bedroom is a room that should be included in such an algorithm or just be
left to a manual TRV control. For this work, the bedroom did not supply any
input that was necessary for the algorithm to achieve the results shown in the
result section of this work.
3.2.6 Naming Conventions for Simulations
Using different simulation setups, models to calculate user feedback and differ-
ent methods within the adaptive algorithm, naming conventions seem appro-
priate to allow for short but unique abbreviations within figures. The following
section explains the naming conventions used throughout this work. All simu-
lations are labeled in the following manner:
⟨LearnMethod⟩⟨UserModel⟩⟨Energydemand⟩-⟨PreheatingMethod⟩(ParametersPreheatingMethod)+(additionalInformation)
Angle brackets indicate compulsory information, normal parenthesis indicate
optional information. Possible Parameters for the LearnMethod, explained in
section 3.1.3, are C, for cluster learning , W for weekly learning and D for daily
learning. Reference cases are labeled with an R. On second position follows the
definition of the behavioral model used, see section 3.2.2. Possible labels are P
and D for the simulations using the adaptive algorithm, the reference cases may
denote the values S and E. P labels the PPD user, D indicates usage of the Daum
63
3 Methodology
user in the learning scenarios. E labels the energy efficient behavior, S the stan-
dard behavior in the reference case. The third position is used to describe the
energy demand of the environment (see section 3.2.3), with the possible values
H for the simulation model with high energy demand, M for medium energy de-
mand, and L for low energy demand. With these three values the reference case
can be described completely. For the simulations using the adaptive algorithm,
the different pre-heating methods explained in section 3.1.2 are given on the
fourth position with the three values S for pre-heating based on a temperature
ramp, B for pre-heating based on a preceding block of increased temperature
and N for no pre-heating. Depending on the value for the pre-heating method
it may follow no value, a number or the letter A. If no pre-heating is used, indi-
cated by a preceding N, no value is given. If pre-heating uses a slope, the follow-
ing number indicates the slope’s inclination as a multiple of 0.1 K/h. If a block
of increased temperature is used, the following number indicates the duration
of the pre-heating block in hours. The letter A indicates usage of the adaptive
pre-heating method described in section 3.1.3. The last parameter is separated
by a “+” sign and optional. It may hold additional information like “H” to indi-
cate a heating system where increased power is used. The letter “R” indicates
usage of the fast, radiative heating system as described in section 3.2.4.
To give an example, a simulation with label “CPM-S5” uses cluster learning with
a user feedback based on the PPD user. The simulation environment uses the
medium energy demand parameterization. Rooms are preheated using a slope
increasing with a fixed value of sph = 0.5 K/h.
3.3 Field test
The adaptive algorithm has not only been tested in simulations but also in a
real-world scenario. This section describes which technology was used in the
field test and how it was evaluated.
64
3.3 Field test
Table 3.5: Size, inhabitants and occupation of the field test participants
Nr. Area Rooms, # occupants occupation
1 90 m2 5 2 working
2 40 m2 2 1 retired
3 50 m2 4 1 working
4 100 m2 5 2 shift work
5 75 m2 5 3 working w. family
6 90 m2 5 4 working w. family
It is most important to mention that in this field test an older version of the
algorithm was under test than used for the simulations. Especially it did not yet
include any method for pre-heating. The weekly learning algorithm was used
as learn method. The field test included six different apartments with a total
of 13 users of different occupation (retired, working, with and without small
children), table 3.5 gives the details.
From a technical point of view, installation should be as easy as possible, there-
fore wireless, battery powered systems10 were used. EnOcean was used as com-
munication standard. Communication between these devices was established
with the help of LabView. The algorithm remained in Python and could be
called from within LabView, utilizing LabPython. The used components were
Laptop The laptop ran the algorithm, ensured communication between the
different components and saved the data. To communicate with the EnO-
cean equipment, an EnOcean capable USB-Dongle was connected to the
laptop. The laptop was also used to display information to the user, for
example if the room was currently heated.
Wireless TRV The wireless MD15-FtL by Kieback & Peter is a battery powered
10with exception of the multisensor SR04. It used a normal power supply
65
3 Methodology
TRV and can send and receive information every ten minutes.
Multisensor A multisensor measured temperature, humidity and CO2 values.
Light Switch A light switch was used as interface between user and algorithm.
It was labeled “too cold” and “too warm”. This was the user’s only inter-
action option with the heating system.
The effects of the algorithm were evaluated by two different methods: To com-
pare the temperature schedule to previous behavior, a base line of measured
temperatures was recorded over the time of one week before installing the adap-
tive system11. Secondly, the users were asked to fill out a questionnaire to gather
information of usability and satisfaction with the system. Due to the small
number of apartments the field test is not representative but may be taken as a
proof of concept.
11While recording the base line the original controls were used to avoid any changes in user behav-ior due to new technology. In every case, basic TRVs were previously used.
66
4 Simulated Results
This section analyzes the results of the simulation runs of the adaptive algo-
rithm compared to the reference case. The results of the reference cases are
presented first, followed by results of the different adaptive measures. For read-
ability and brevity some results of different setups (e. g. energy demand, differ-
ent rooms) may be omitted or shown only in the appendix if they are in agree-
ment with the results shown in this chapter.
4.1 Results of the Reference Cases
The reference cases are analyzed for room temperatures, thermal comfort and
energy consumption. Figure 4.1 shows the simulated air temperatures in the
living room for the medium energy scenario as carpetplot, with days on the x-
axis and timesteps on the y-axis. The temperature value is color-coded. The re-
sults of the other rooms are comparable, with exception of the bedroom with its
lower temperature set-point. On the left side of figure 4.1 the RSM (i.e. reference
case, standard behavior, medium energy demand as explained in section 3.2.6)
case shows the two different temperature set-points of 18 ◦C during the night
setback between 23:00 h and 06:00 h and 21 ◦C the rest of the day. The right side
shows the REM case where a room is only heated on demand. Times of usage
are discernible from the times without usage. The REM case shows on average
lower room temperatures, also in times of user presence. This is due to the high
thermal inertia of the building. It should be observed that the temperature set-
point of 21 ◦C on presence is mostly not met. At day 34, solar radiation leads to
67
4 Simulated Results
Living Room
Day 1 Day 15 Day 29
RSM
00:00
08:00
16:00
23:50Day 1 Day 15 Day 29
REM
17.4
18.0
18.6
19.2
19.8
20.4
21.0
21.6
22.2
T in ◦C
Figure 4.1: Simulated air temperature of the reference cases in the living roomfor medium energy demand. On the left side the standard user be-havior leads to two different, clearly distinguishable temperatureblock. On the right side the times of user presence are clearly dis-cernible although temperatures at user presence are rather low. Atday 34 high solar gains lead to increased room temperatures.
an increase in room temperature for both reference cases, temperatures then
exceed the temperature set-point of 21 ◦C.
Figure 4.2 shows the deviations between temperature set-point and the simu-
lated temperatures for each room and both behavioral models, on the left side
again the standard reference case, on the right side the energy efficient one. For
the RSM case the simulated temperatures are slightly below the set-point for all
rooms except the bedroom at daytime. At nighttime all rooms do not cool be-
low their set-point temperatures. This persisting deviation from the set-point
is due to the simple P-controller used in TRVs. If the room is heated the TRV
already starts closing before the setpoint of 21 ◦C if the room cools down the
TRV reheats the room before the set-point temperature is reached. That the
living room and study sometimes exceed their set-point temperature is due to
68
4.1 Results of the Reference Cases
Bath
Bedroom
Kitchen
Living Room
Study
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
Day 1 Day 15 Day 29
RSM
00:00
08:00
16:00
23:50Day 1 Day 15 Day 29
REM
−6.0
−4.5
−3.0
−1.5
0.0
1.5
3.0
4.5
6.0∆ T in K
Deviations of air temperature from set points
Figure 4.2: Deviation of the air temperature from its set-point for all rooms formedium energy demand. The RSM case on the left shows that themeasured temperatures are slightly below the set-point if the user ispresent, the REM case on the right shows that the temperatures areusually too cold if the user is present. If the user is absent, tempera-tures are slightly too warm.
69
4 Simulated Results
solar gains. Deviations of the temperature set-point are normally in the range
of 1 K-1.5 Kfor the RSM case.
Applying the efficient user behavior, all rooms are again too warm while the
user is absent. The rooms do not cool down as far as the set-point. If the user
is present, the temperatures are below the set-points, with the bedroom once
again as exception. As the valves where usually fully opened, the heating sys-
tem is not powerful enough to reheat the rooms on time. The deviations from
the set-point are much larger than in the standard case with up to 4 K and will
affect the thermal comfort of the user. Simulations using the setup with low or
high energy demand show the same results for standard and efficient behavior.
The deviations are less pronounced in both cases because the rooms are better
insulated resulting in warmer rooms even if it is unheated (low energy scenario)
or because the heating system is more powerful (high energy scenario).
In figure 4.3 a boxplot represents the different distributions of the simulated
temperatures for the six reference cases. In a boxplot, the box itself covers the
second and third quartile of the data, i. e. 50 % of the data are present within
this box. The line in the box represents the median, not the mean, of the data
and separates the second and third quartile. The whiskers at the end of the box
show the extend of the first and fourth quartile. The maximum length of these
whiskers is 1.5 times the inner quartile distance. Values outside this range are
considered as outliers and marked as single dots above the whiskers1. The up-
per part of figure 4.3 shows the distribution of the simulated temperature for all
rooms, the lower figure the distributions for all rooms except the bedroom. As
mentioned in section 3.2.5 including the bedroom strongly influences the re-
sults of the measured temperature distribution. The median of all simulated
temperatures is lower if the bedroom is considered, due to its lower tempera-
ture set-points. The effect on the median value of simulated temperatures is
approximately 1 K throughout all reference cases.
1Different definitions exist concerning the length of the whiskers. In this work matplotlibs defaultvalue of 1.5 times the inner quartile distance is used.
70
4.1 Results of the Reference Cases
16
18
20
22
24
Air
tem
per
atu
rein
°C
Tsim for all rooms if user present
RSL RSM RSH REL REM REH
16
18
20
22
24
Air
tem
per
atu
rein
°C
Tsim for all rooms but Bedroom if user present
Figure 4.3: Simulated air temperatures for the different reference cases whenthe user is present. The simulated temperature is higher approxi-mately 2 K higher for the standard user. Ignoring the bedroom leadsto higher average temperatures but the temperature distributionsfor the efficient user indicate a low thermal comfort.
As figure 4.2 has shown, the bedroom is slightly above its temperature set-point,
therefore it can be expected that thermal comfort is in most cases ensured.
All other rooms are normally below their temperature set-point if the user is
present, which will affect thermal comfort. Therefore the analysis will focus on
the lower part of figure 4.3. The simulated temperatures for the efficient be-
71
4 Simulated Results
Table 4.1: Summary of the results for the reference case for the standardbehavior
RSL RSM RSH
Qsim [kWh] 280.31 418.49 444.89
PPD criteria not met [h] 3.83 2.33 1.00
Thereof too cold [h] 0.00 2.33 1.00
Daum criteria not met [h] 46.50 132.83 92.50
Thereof too cold [h] 46.50 132.83 92.50
havior are considerably lower than for the standard behavior. The median is
approximately 2 K lower. For the efficient case more than 50 % of the measure-
ments are in a temperature range between 18.5 ◦C and 19.5 ◦C while the user is
present and therefore significantly lower than the set-point values. For the stan-
dard behavior model the simulated temperature models are centered around
21 ◦C, in good accordance with the set-point. Given a preferred temperature
of 21 ◦C for the PPD user and 23 ◦C for the Daum user, one can expect accept-
able to good thermal comfort for the two behavioral models for the standard
behavior but rather poor comfort ratings for the efficient behavior. It also infers
that the heating system is powerful enough for a constant heating scenario, but
may fail at intermittent heating, which emphasizes the need of a pre-heating
algorithm as discussed in section 3.1.2. It can also be seen that the temperature
distribution for the RSL case is different from the other standard cases, with its
median approximately 0.5 K higher, due to insulation and lower air exchange.
Tables 4.1 and 4.2 summarize the results for the standard and the efficient be-
havior case respectively. The simulated energy demand Qsim for the standard
behavior is higher than the one for the energy efficient behavior. For the low
energy scenario the difference is approximately 27 %, for the other two scenar-
ios approximately 20 % based on the standard behavior values as reference. The
72
4.1 Results of the Reference Cases
Table 4.2: Summary of the results for the reference case for the efficientbehavior
REL REM REH
Qsim [kWh] 205.24 333.92 352.50
PPD criteria not met [h] 230.17 277.00 260.33
Thereof too cold [h] 230.17 277.00 260.33
Daum criteria not met [h] 295.17 297.17 296.17
Thereof too cold [h] 295.17 297.17 296.17
energy demand of the low energy scenario is more than 100 kWh lower than the
energy demand of the medium scenario while the energy demand of the high
demand scencario is 30 kWh higher.
Comfort must be rated with respect to the total simulation time of 1008 h or
better the 635 h the user is present. The time the PPD comfort criteria is not
met is negligible with a maximum of 4 hours for the low energy demand case
for the standard behavior. The Daum criteria is less often met. This is due
to the fact, that the preferred temperature of the Daum user is approximately
2 K higher than the preferred temperature of the PPD user. With comfort not
met for approximately 50 hours the low energy scenario shows the best results
in the standard behavior cases. Due to the more powerful heating system, the
comfort criteria is better met for the high energy scenario than for the medium
energy scenario, a result that is supported by the slightly higher simulated room
temperatures shown in figure 4.3. Very good comfort for the PPD user and ac-
ceptable comfort for the Daum user in the standard case have already been
assumed by the simulated temperatures distribution shown in figure 4.3.
With respect to energy efficient behavior, the comfort results are worse in com-
parison to the standard behavior. The PPD based user model evaluates approx-
imately 22-27 % of the time below the defined comfort threshold, far worse than
73
4 Simulated Results
in the standard case. The low energy scenario again shows the best comfort re-
sult, the medium energy scenario the worst results and the high energy scenario
scores slightly better due to the more powerful heating system. For the Daum
user the thermal comfort is not meet for approximately 300 hours independent
of the energy demand. This is an increase of 2.5-6 compared to the standard
case. These 300 h are close to the 316 h the user is present and awake.
To conclude this section: Two different reference cases have been shown. One
case uses continuous heating and ensures high thermal comfort while the other
case uses intermittent heating and shows low thermal comfort. The standard
behavior is the upper limit for energy demand as it provides high thermal com-
fort, more energy may only be used if the thermal comfort is simultaneously
increased, which is, at least for the PPD user, barely possible. The efficient be-
havior shows a lower limit for comfort and energy demand. While it uses less
energy than the standard case, the comfort is also far lower. The results of the al-
gorithm are expected to be between those two reference cases: Comfort should
be much better than in the energy efficient case while less energy than in the
standard case should be used. As the efficient behavior uses 20 % less energy
than the standard behavior, this is an upper bound to possible energy savings.
4.2 Results of the base algorithm with two di�erent user
types
This section describes the results of the base algorithm as described in sec-
tion 3.1.1 without any advanced measures. It is evaluated with respect to ther-
mal comfort, efficiency of the feedback system and energy consumption. All
results are shown for the medium energy demand only but the results are simi-
lar to the other two scenarios.
74
4.2 Results of the base algorithm with two different user types
00:00 00:0003:00 06:00 09:00 12:00 15:00 18:00 21:00
Time of the day
15
16
17
18
19
20
21
Tem
per
atu
rese
tpo
inti
n◦ C
Tset,initial
Tset,runtime
Tset,end of day
Figure 4.4: Effect of the algorithm on the initial profile. The red line indicatesthe initial profile, the blue line the temperature at runtime and theorange line the temperature at the end of the day.
4.2.1 Evolving pro�le and temperature set-points
The first to figure show a detailed view on the evolution of a temperature set-
point profile. All values shown in this figures are taken from the CPM-N simu-
lation.
Figure 4.4 shows the effect of the algorithm on the first day of the simulation.
The red line shows the initial temperature Tset, initial at the beginning of the
day. At the start of the algorithm it is set to 21 ◦C for every timestep. The blue
line indicates the temperature at runtime, Tset, runtime, which is the tempera-
ture set-point sent to the TRV. Finally the orange line shows the temperature
at the end of the day, which will become Tset, initial for the following day. The
stochastic effects of the algorithm result in the unsteady line for Tset, runtime and
Tset, end of day. As each change in the algorithm normally spans over several time
steps, the effects of each change are superposed, resulting in different values
75
4 Simulated Results
21:00 22:0021:15 21:30 21:45 22:15 22:30 22:45
Time of the day
8
10
12
14
16
18
20
Tem
per
atu
rese
tpo
inti
n◦ C
Tset,initial
Tset,runtime
Tset,end of day
Too Cold Feedbacks
Figure 4.5: Effect of a given user feedback on the temperature profile. Due tolow measured temperatures, all feedbacks at the 21 ◦C were ignored.The two feedbacks at 21:20 and 22:20 increase the temperature set-point.
for the temperature at runtime and the finally stored temperature set-point
at the end of the day. The algorithm decreases the initial temperature for the
first day by approximately 1 K(T̄set, runtime = 19.8±0.7◦C), and for the next day,
the initial temperature is already reduced by approximately 2 K(T̄set, end of day =19.1±0.8◦C).
Figure 4.5 gives a more detailed view on the evolution of the temperature pro-
file. This time the temperature profile for the 14th day of the algorithm is shown.
Besides the three previous temperatures, also the given “too cold” feedbacks
are shown. First of all it can be noticed that the the Tset, runtime is never be-
low Tset, initial, indicating that no systematic decreases take place. This is due to
two reasons: The probability for a systematic decrease is lower at day 14 com-
pared to the first day. Furthermore the setpoint temperatures are already low.
If the measured temperature is 2 K above the set-point, no systematic decrease
76
4.2 Results of the base algorithm with two different user types
will occur. The “too cold” feedbacks 2-5 are ignore by the algorithm, because
the temperature set-point is already 2 K above the simulated temperature (the
CPM-N version without any pre-heating measures has the same problems as
the REM version in supplying sufficient temperatures if the user is present). The
first and the last feedback affect the profile. Because feedbacks are evaluated at
the end of the timestep, the temperature set-point at runtime is not increased.
As the set-point temperature at runtime is 15 ◦C, the temperature is increased
to its default value of 21 ◦C. Even if the effects of the given feedback are limited
given the temperature at runtime, the effects for the temperature set-point at
the end of the day are more obvious, increasing the width of the temperature
peak by 30 minutes for the next day.
To show which user feedbacks and systematic decreases led to the final tem-
perature profile, figure 4.6 shows the feedbacks given to the system. It shows a
grid of color coded carpetplots with five rows and two columns. Each row rep-
resents one room, each column one behavioral model (Daum or PPD). Within
these carpets, each day is represented as one column and every timestep as one
row. A gray entry indicates a systematic decrease by the adaptive algorithm, a
white entry that the temperature profile was unaltered because no feedback by
the user was given and a systematic decrease was not applicable. Red entries
indicate a user complaint about the room being “too warm” and blue entries
feedback about the room being “too cold”. Orange and green indicates that the
feedback was given but ignored by the system because counter measures were
already taken. For a “too cold” feedback this can occur if the set-point temper-
ature is already higher than the simulated temperature. At this point the user
feedback is attributed to the lower room temperature. Increasing the tempera-
ture set-point would not improve the user’s comfort.
For each case in figure 4.6 there are many systematic decreases at the beginning
but over time they become less frequent resulting in mostly no changes to the
temperature profile. There are two reasons for this effect: first, the probability
for a systematic decrease is reduced over time as explained in section 3.1. Sec-
77
4 Simulated Results
Bath
Bedroom
Kitchen
Living Room
Study
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
Day 1 Day 21 Day 41
CDM-N
00:00
08:00
16:00
23:50Day 21 Day 41
CPM-N
SystematicDecrease
Too Cold
IgnoredToo Cold
No action
IgnoredToo Warm
Too Warm
Feedbacktype
Figure 4.6: Feedbacks provided by the user to the system for medium energydemand. Systematic decreases occur less often over time. TheDaum user on the left complains more often about the temperaturebeing too low. Many of those feedbacks are ignored by the algo-rithm, because it already tries to increase room temperature.
78
4.2 Results of the base algorithm with two different user types
Living Room
Day 1 Day 21 Day 41
CDM-N
00:00
08:00
16:00
23:50Day 21 Day 41
CPM-N
SystematicDecrease
Too Cold
IgnoredToo Cold
No action
IgnoredToo Warm
Too Warm
Feedbacktype
Figure 4.7: Feedbacks provided by the user to the living room. The feedback ofthe Daum user on the left is mostly ignored by the algorithm, thePPD user on the right gives less feedback and is less often ignored.
ond, the temperature’s set-point is already 2 K below the simulated temperature
because of the room’s thermal inertia. At this point the TRV is already closed
rendering a further set-point reduction not reasonable. A “too warm” feedback
was never given in any room which is a good result because this indicated that
the system does not overheat the room. On the other hand, both users show
high numbers of complaints about the room being “too cold”.
79
4 Simulated Results
For a more detailed view, the feedbacks for the living room are shown again in
figure 4.7 for both user models. The Daum user on the left side tends to give
feedback for hours in a row, especially in the evenings in the living room, but
most of them are ignored by the system, which is only possible if the tempera-
ture set-point is already higher than the simulated temperature. This indicates
that the algorithm understood the user’s demand for higher temperatures but
the heating system has difficulties to comply with the user’s demand. The PPD
user gives far less feedbacks, but still several of those are ignored, indicating
again a problem with reheating the rooms on time.
That the system was able to understand the user’s demand can be seen by the
distribution of the temperature set-points in figure 4.8. The upper axes show
the temperature distributions if the user is present for all rooms but the bed-
room. In the case of user presence, the range of the temperature set-points is
wider for the results from the PPD user and on average lower than the results
based on the Daum user. This is an effect of the far more feedbacks given by the
Daum user, resulting in a higher temperature set-point. The PPD user shows a
median value for the temperature set-point of 20 ◦C for the medium and high
energy demand, the median for the low energy demand is at approximately
19 ◦C. All distributions extend up to the comfort range of the user but also in-
clude lower temperature set-points that must be considered as uncomfortable.
The Daum user in contrast shows a very narrow distribution at approximately
21 ◦C. The temperature set-points for both reference cases are 21 ◦C if the user
is present.
The lower axes show the temperature set-point distribution if the user is absent.
At this time the behavioral model has no direct influence on the temperature
profile thus the distributions of the set-points are now more alike. Some differ-
ences still exist as user feedback is not limited to presence only and may also
show artifacts from the previous day, if the user was present then. The algo-
rithm aggressively lowers the room temperatures, therefore the temperatures
set-point median is approximately 1.5 K to 2 K lower than in the REM case. This
80
4.2 Results of the base algorithm with two different user types
10
12
14
16
18
20
22
24
Tset if user present for all rooms but bedroom
CDL-N CDM-N CDH-N CPL-N CPM-N CPH-N RSM REM
10
12
14
16
18
20
22
24
Tset if user absent for all rooms but bedroom
Figure 4.8: Distribution of the setpoint temperatures for the algorithm withoutpre-heating measures. If the user is present, the many feedbacks ofthe Daum user lead to an higher temperature set-point comparedto the PPD user. If the user is absent, the distributions are similarfor both behavioral models.
indicates that reheating the rooms on time will be even more difficult than in
the REM scenario.
81
4 Simulated Results
Living Room
Day 1 Day 21 Day 41
CDM-N
00:00
08:00
16:00
23:50Day 21 Day 41
CPM-N
−8−6−4−202468
∆ T in KDeviations of air temperature from set points
Figure 4.9: Difference between simulated temperature Tmeas and set-pointtemperature Tset for the living room and medium energy demand.For both behavioral models the set-point temperatures are not metin the evening if the user is present as well as on the weekend duringthe day.
4.2.2 Simulated temperatures vs. temperature set-points
Figure 4.9 shows the temperature deviations between set-point and simulated
temperature for the living room. In times without usage the simulated tem-
peratures are higher than the set-point, often in the range of 2 K, similar to the
results from the REM case. At this temperature difference the TRV will be closed
and the room will be unheated. Due to the lower temperature set-points, the
overheating of the living room due to solar radiation around day 34 is more pro-
nounced than in the REM case. While occupied, the rooms are usually cooler
than the set-points. The deviations are often similar for the Daum and PPD
user, with exceptions especially around day 10 in the evenings, where the tem-
perature set-point is less well met for the Daum user. As in the REM case, re-
heating the room from the lower temperatures takes too long to ensure thermal
82
4.2 Results of the base algorithm with two different user types
12
14
16
18
20
22
Tsim if user present for all rooms but bedroom
CDL-N CDM-N CDH-N CPL-N CPM-N CPH-N RSM REM
12
14
16
18
20
22
Tsim if user absent for all rooms but bedroom
Figure 4.10: Distribution of the simulated temperatures for the algorithm with-out pre-heating measures. In times of presence the RSM caseshows higher simulated temperatures than any other case. But thetemperature distributions are also below the REM case, indicat-ing even lower thermal comfort. The clear set-point differencesbetween Daum and PPD user cannot be seen in the simulatedtemperatures.
comfort. For both users the temperature set-point cannot be reached by a high
margin which will impact the user’s thermal comfort.
Difficulties with re-heating the room and impact on the user’s thermal comfort
83
4 Simulated Results
can also be seen in the more general temperature distributions for all rooms
but the bedroom for the different cases shown in figure 4.10. It can be observed
that the simulated temperatures while the user is present are significantly lower
than in the RSM and even the REM case. With respect to the already low ther-
mal comfort in the REM case, even worse thermal comfort must be expected
for the algorithm controlled cases. Between the simulations, it can be observed
that the simulated temperatures in cases with the PPD user are a bit below the
cases using the Daum user. Again, simulated temperatures are highest in the
low energy scenario and the high energy demand scenario shows slightly higher
simulated temperatures than the medium demand one. The median values for
the simulated temperature are 2-3 K below their respective set-points shown in
figure 4.8. This indicates that, in times of presence, the TRV will be completely
open and supply its maximum power to the room. If the user is absent (lower
part of figure 4.10), the simulated temperatures are lower than both reference
cases, and in between the simulations, the medium energy demand case shows
the lowest median value for the simulated temperature, although it is very close
the the high energy demand cases. The median value of the simulated tempera-
ture is in each case approximately 2 K above the set-point, which indicates that
the TRV is often closed while the user is absent and the rooms are unheated.
4.2.3 Detailed user feedback
To further analyze the feedback given by the user, figure 4.11 shows the given
and ignored feedbacks for each day. As indicated by the carpetplots in fig-
ure 4.6, the Daum user gives more feedback than the PPD user and also the ra-
tio of ignored feedbacks is higher. This means the feedback probably wouldn’t
have been necessary if the set-point temperature would have been reached. As
stated before, this indicates a problem with re-heating the room on time. On
weekends, the number of feedbacks is higher, which is not surprising as the user
spends more time at home on weekends. This leaves him with more opportu-
84
4.2 Results of the base algorithm with two different user types
Day 1 Day 21 Day 41
CDM-N
0102030405060708090
Day 1 Day 21 Day 41
CPM-N
Nu
mb
ero
ffee
db
acks
Number of too cold feedbacks per Day
too cold ignored too cold
Figure 4.11: Feedbacks given by the user to the system. Left: Daum user, Right:PPD user. Red are the total “too cold” feedbacks given, grey arethe thereof ignore feedbacks. The amount of ignored feedbacks israther high, indicating problems with the heating system.
nities to give feedback. An overall decline of given feedbacks would indicate
a working algorithm but this can hardly be found. The PPD user shows fewer
feedbacks at the end of the simulation, but this is due to the increased ambi-
ent temperature. The feedback values shown are the values for the medium
energy demand case. These results are slightly worse than the other cases (see
figures A.5 and A.6), but the overall behavior is similar. Although the number
of feedbacks seems very high, only the difference between total feedbacks and
ignored feedback should be counted, due to the simulated user behavior: The
user will complain each timestep, even if the room is already reheating. This be-
havior would not be likely for a real person, therefore the ignored feedbacks are
not considered, although these could have been used to confirm the user pres-
ence. The high number of ignored feedbacks confirms the assumption that the
low thermal comfort in the base case is not an effect of a failing algorithm but
85
4 Simulated Results
Table 4.3: Results of the base case
CDM-N CPM-N
Qsim [kWh] 250.84 246.61
Qsim relative to RSM [%] 60 59
PPD criteria not met [h] - 327.50
Thereof too cold [h] - 327.50
Daum criteria not met [h] 302.50 -
Thereof too cold [h] 302.50 -
PPD criteria relative to REM [%] - 118
Daum criteria relative to REM [%] 102 -
Total number of feedbacks 458 297
more due to the heating system. Therefore measures were taken to make the
algorithm more compatible with the heating system than in the base case by
adding a pre-heating sequence.
The key results are summarized in table 4.3. Compared to the RSM case, the
energy demand of the adaptive behavior is lower. And even compared to the
REM case approximately 80 kWh less energy is used over the course of the sim-
ulation. This can only be achieved if the temperatures are lower than in the ref-
erence case. The comfort is also reduced, but less than one would expect due
to the energy savings: For the PPD user the time outside the comfort threshold
increases approximately 20 %, for the Daum user the difference is of no signif-
icance compared to the REM case. Given the already low level of comfort in
the REM case, the potential for less satisfaction was limited. The low simulated
temperatures indicate that the thermal discomfort is likely more pronounced
in the algorithm case than in the reference cases. The PPD allows judging ther-
mal comfort using the PMV value. Summing up all values provides a value to
compare the total comfort, with lower values being better. And, as expected, in
86
4.2 Results of the base algorithm with two different user types
the REM case the summation of the absolute value of the PMV results in 1619,
while the CPM-N case scores 2556. So not only the time of low thermal com-
fort increased but also the severity. With 458 or 297 feedbacks respectively, the
number of necessary interactions is quite high, on average 10 times per day for
the Daum user. Counted are only valid feedbacks, i. e. if a given feedback was
not ignored by the algorithm because the algorithm was already taking care to
improve the thermal comfort. With respect to figure 4.11 this value corresponds
to the sum of the red-shaded area.
It can be concluded that the adaptive algorithm without any additional mea-
sures falls short of ensuring the user’s thermal comfort. The large energy sav-
ings directly translate into a measurable decrease of thermal comfort. While the
user is absent, the algorithm decreases the temperatures below the 16 ◦C used
in the REM case, resulting in cold rooms that cannot be reheated fast enough
when the user is present. Nonetheless, the Daum user leads to acceptable set-
point temperatures if the user is present. If fewer feedbacks are given, as it is the
case with the PPD-based user, the temperature set-points degrade into uncom-
fortable temperature set-points. However, it will have to be seen if this result is
not partially connected with the rooms being too cold and therefore feedbacks
are ignored by the algorithm. As the algorithm has shown severe problems with
reheating the room if the user is present, addressing this issue should be prior-
itized.
4.2.4 Pro�le detection by the algorithm
Although the algorithm falls short on providing thermal comfort to the user,
this is mainly due to limitations of the heating system. The algorithm’s ability
to detect the user’s presence has not been analyzed so far. To assess the algo-
rithms general ability to detect the user behavior, a static presence profile is
used as shown in figure 4.12. It has two different profiles, one for the weekdays
and one for the weekend. The temperature set-point is a good measure to an-
87
4 Simulated Results
Day 1 Day 8 Day 15 Day 22 Day 29 Day 36 Day 43Date
00:0002:0004:0006:0008:0010:0012:0014:0016:0018:0020:0022:00
Tim
e
Bedroom
Bath
Kitchen
Living Room
Study
absent
Figure 4.12: Static presence profile to assess the algorithm’s general ability todetect the user behavior. Two differente profiles for weekdays andweekend are used
alyze the matching of presence or not. The temperature set-point distribution
determines the threshold value for Tset that marks the user as present or not.
Figure 4.13 shows a histogram of the Tset values. Due to the partial randomness
of the systematic decreases a normal distribution is the first assumption made
for the temperature distribution. But as user feedback will increase the tem-
perature set-point a right skewed normal distribution must be expected as fig-
ure 4.13 shows. To improve the curve fitting process for the left part of the figure,
the fitting tool2 considers only data below 21 ◦C, because this is the minimum
temperature set-point if the user gives a “too cold” feedback. The tempera-
ture which frequency exceeds the theoretical amount determines the threshold
temperature Tset, th, which is indicated as grey bar in figure 4.13.
Figure 4.14 shows that the state matching works well with the estimated thresh-
old. In the first few days the matching quality is rather low because the initial
2For fitting purposes, the scipy.stats package is used
88
4.2 Results of the base algorithm with two different user types
8 10 12 14 16 18 20 22 24 26
Temperature in ◦C
0.00
0.05
0.10
0.15
0.20
0.25
Rel
ativ
eFr
equ
ency
PPD TH0.5
8 10 12 14 16 18 20 22 24 26
Temperature in ◦C
Rel
ativ
eFr
equ
ency
Daum S1
Figure 4.13: Histogram of the temperature set-point distributions for the stan-dard PPD user on the left and Daum users on the right. The lineshows the fitted skewed normal distribution and the grey bar indi-cates the assumed threshold temperature for presence.
set-point temperature is 21 ◦C, therefore absence is not correctly matched. But
after a few days the results improve as systematic decreases lower the tempera-
ture set-point if the user is absent. After approximately a week, the user’s states
are matched better than 90 % on average. The Daum user shows a remaining
deviation at the weekends due to the temperatures not being lowered during
the user’s absence. The Daum user requires higher temperatures for his ther-
mal comfort, as shown in the temperature set-point distribution in figure 4.13,
resulting in higher measured temperature values. If a room is heated to 24 ◦C it
will keep its higher temperature even after closing the valve and the algorithm
will not lower the temperature set-point below 22 ◦C because no energy savings
can be expected due to the already closed TRV. At the weekend, the living room
is occupied in the afternoon leading to higher measured temperatures that per-
sist longer due to the room’s south-west facing large windows and heat gains by
89
4 Simulated Results
0 5 10 15 20 25 30 35 40
Days
0.0
0.2
0.4
0.6
0.8
1.0
Perc
enta
geo
fmat
ched
stat
e
PPD
Daum
Figure 4.14: Percentage of successful presence and absence matching for thetwo users used throughout this simulation. User states arematched very well, indicating a good working algorithm.
solar radiation.
4.3 Adding pre-heating sequences
As shown in the previous section, the largest threat to user comfort by the al-
gorithm is the thermal inertia of the room because it cannot reheat on time.
To avoid problems with reheating, the pre-heating measures described in sec-
tion 3.1.2 were added to the base algorithm. First, the results of the slope based
pre-heating are discussed, afterwards, the results using the block based pre-
heating. Finally the adaptive pre-heating measures discussed in section 3.1.3
are evaluated.
90
4.3 Adding pre-heating sequences
4.3.1 Pre-Heating with a temperature slope
The first pre-heating method uses a slope to increase the room temperature
prior to an expected usage of the room. The temperature set-point is increased
by 0.5 K every hour.
The temperature set-points for each time step and room after applying the
slope based pre-heating method are shown in figure 4.15. Due to differences of
several Kelvin between temperature set-points when the user is present or ab-
sent, pre-heating slope take several hours. The kitchen is normally used in the
morning and starts pre-heating already at night to reach an acceptable temper-
ature at daybreak. The living room shows a pre-heating slope in the morning
and in the evening. A systematic temperature decrease is not allowed while
the room re-heats, this results in the temperature profiles gaining some simi-
larity. This similarity can be explained by the design of the pre-heating mech-
anism: if the first timestep of a higher temperature block is not reduced by the
algorithm, the next day will show exactly the same pre-heating slope. Every
difference between days at lower temperatures will also be overwritten by the
pre-heating slope, further increasing the similarity of the results. Differences
between weekdays and weekends are still well discernible, indicating that the
algorithm is still able to adapt to different situations.
Figure 4.16 shows the different temperature set-point distributions for the sim-
ulations with pre-heating alongside the simulations without. Temperature set-
point distributions while a user is present are shown on top. For both users the
median value of the temperature set-point is increased by approximately 1 K.
With 21 ◦C for the PPD user and 22 ◦C for the Daum users the median value is
now close to their expected preferred temperature. Overall, these differences
should lead to better thermal comfort if these set-point values can be reached.
As the pre-heating should take place while the user is absent, more change has
to be expected in the lower part of figure 4.16. While the user is absent, the tem-
perature set-points are vastly different from the cases without pre-heating. The
91
4 Simulated Results
Bath
Bedroom
Kitchen
Living Room
Study
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
Day 1 Day 15 Day 29
CDM-S5
00:00
08:00
16:00
23:50Day 1 Day 15 Day 29
CPM-S5
12.0
13.5
15.0
16.5
18.0
19.5
21.0
22.5
24.0
Temperature in ◦C
Figure 4.15: Temperature set-points for each time step with the slope basedpre-heating method for the medium energy scenario. The addi-tion of a temperature slope leads to profiles that gain similarity be-tween the days. The different clusters (weekend, weekday) are stilldiscernible.
92
4.3 Adding pre-heating sequences
10
12
14
16
18
20
22
24
Tset if user present for all rooms but bedroom
CDM-N CPM-N CDM-S5 CPM-S5
10
12
14
16
18
20
22
24
Tset if user absent (for all rooms but bedroom
Tem
per
atu
rein
◦ C
Figure 4.16: Temperature set-point distributions for the slope based pre-heating for the medium energy scenario. Pre-heating takes placeif the user is absent, resulting in clearly higher temperature set-points. But also if the user is present, the temperature set-pointare increased.
median temperature set-point is approximately 6 K above the base case for the
Daum user and approximately 5 K for the PPD user. Also the boxes show no
overlap with their respective case without pre-heating, indicating a very differ-
ent distribution. This increased temperature while the user is absent may result
in higher simulated temperatures while the user is present.
93
4 Simulated Results
12
14
16
18
20
22
24Tsim if user present for all rooms but bedroom
CDM-N CPM-N CDM-S5 CPM-S5
12
14
16
18
20
22
24Tsim if user absent for all rooms but bedroom
Tem
per
atu
rein
◦ C
Figure 4.17: Distributions of the simulated temperatures for the cases withand without slope based pre-heating. The pre-heating methodachieves it objective: The increased temperatures if the user is ab-sent translate into higher temperatures if the user is present forboth behavioral models.
To evaluate this assumption, the distributions of the simulated temperatures
are shown in figure 4.17. The median value has increased by 2 K for both behav-
ioral models if the user is present. This sets the simulated temperatures close to
the preferred user temperatures, although it may be experienced as a bit cold. If
the user is absent, the effect is more pronounced, the difference between cases
94
4.3 Adding pre-heating sequences
Day 1 Day 21 Day 41
CDM-S5
0
10
20
30
40
50
60
70
Day 1 Day 21 Day 41
CPM-S5
Nu
mb
ero
ffee
db
acks
Number of too cold feedbacks per Day
too cold ignored too cold
Figure 4.18: Development of the user feedbacks for simulations using slopebased pre-heating. Due to pre-heating the ratio of ignored feed-backs is lower than in the case without any pre-heating.
with and without pre-heating is approximately 4 K. The distributions if the user
is present or absent get more similar, the median simulated temperature if the
user is away is only approximately 1 K below the simulated temperatures if the
user is present. This difference is far smaller compared to the approximately 3 K
in the base-case without pre-heating method.
The influence of the increased temperatures on the user feedbacks is shown
in figure 4.18. Both behavioral models show a considerably decreased num-
ber of feedbacks and at least the PPD user simulations show a clear decrease
in user feedbacks, indicating a working learning method as compared to fig-
ure 4.11. Furthermore the ratio of ignored feedbacks is also reduced, indicating
the positive impact of the pre-heating method on the algorithm. The effect on
the PPD user is way better because his comfort temperature is below the Daum
ones. Reaching the comfort temperature of the Daum user is still difficult for
the heating system.
95
4 Simulated Results
Table 4.4: Results of the simulations using the first pre-heating method
CDM-S5 CPM-S5
Qsim [kWh] 342.37 339.01
Qsim relative to RSM [%] 82 81
PPD criteria not met [h] - 119.50
Thereof too cold [h] - 119.50
Daum criteria not met [h] 168.83 -
Thereof too cold [h] 168.83 -
PPD criteria relative to REM [%] - 43
Daum criteria relative to REM [%] 57 -
Total number of feedbacks 511 85
The results of the slope based pre-heating method are summarized in table 4.4.
The pre-heating method results in an increased energy demand of approxi-
mately 90 kWh or 33 % for both cases compared to the case without pre-heating.
This increase in energy demand brings them close to the energy demand of the
REM case (334 kWh, see table 4.2) and 80 kWh below the energy demand of the
standard behavior (418 kWh, see table 4.1 on page 72). While the energy de-
mand increases, the thermal comfort is improved. Compared to the cases with-
out pre-heating, the comfort criteria is violated only half of the time (170 h vs.
300 h) for the Daum user. For the PPD user, the PPD criteria is also violated less
often (120 h vs. 330 h). The increased energy demand is therefore used to im-
prove the user’s thermal comfort. Compared to the REM case the thermal com-
fort is roughly improved by 50 % for both cases, based on the times the comfort
is below its threshold value. For the Daum user the comfort comes even close
to the RSM case (170 h vs. 130 h), but the thermal comfort for the PPD user is
still better although it consumes 20 % more energy.
The slope based pre-heating method helped to decrease the necessary feed-
96
4.3 Adding pre-heating sequences
backs for the PPD user by 70 % to only two feedbacks per day, this is in line with
the improved comfort. The number of feedbacks for the Daum user increased
instead, despite a significant improvement in thermal comfort. This is mainly
an effect of the evaluation method that counts only valid feedbacks: Because
of the higher temperatures realized when the user was present less user feed-
back was ignored. The results for the other energy scenarios are similar and the
result tables are shown in section A.1.
Overall, the slope based pre-heating method is capable to significantly improve
the user’s thermal comfort by increasing the temperature set-points prior to the
expected use of the room. The energy demand of this pre-heating method is
similar to the energy demand of the REM case, but provides far better thermal
comfort. The slope-based pre-heating method is a clear improvement over the
algorithm without any pre-heating.
4.3.2 Pre-Heating with an increased temperature block
In contrast to the slope based pre-heating method, this method uses a con-
stant temperature for a fixed time before the expected user presence. For test-
ing purpose, four different pre-heating times were used from one hour up to
four hours.
The resulting temperature set-points for a one-hour pre-heating phase are shown
in figure 4.19. In contrast to the slope based pre-heating method the times of in-
creased temperature are more distinct. The kitchen for example shows one us-
age time in the morning and the evening while the Living Rom shows mostly an
increased temperature set-point in the evening. But similarly to the slope based
pre-heating, the days gain similarity compared to the case without pre-heating.
Again, differences between weekdays and weekend can clearly be seen.
The temperature set-point distributions are shown for the cases with and with-
out pre-heating in figure 4.20. If the user is present, the temperature set-point is
97
4 Simulated Results
Bath
Bedroom
Kitchen
Living Room
Study
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
00:00
08:00
16:00
23:50
Day 1 Day 15 Day 29
CDM-B1
00:00
08:00
16:00
23:50Day 1 Day 15 Day 29
CPM-B1
10
12
14
16
18
20
22
24
Temperature in ◦C
Figure 4.19: Temperature set-points for each time step with the block basedpre-heating method for the medium energy scenario
98
4.3 Adding pre-heating sequences
10
12
14
16
18
20
22
24
Tset if user present for all rooms but bathroom
CDM-N CPM-N CDM-S5 CPM-S5 CDM-B1 CPM-B1
10
12
14
16
18
20
22
24
Tset if user absent for all rooms but bathroom
Tem
per
atu
rein
◦ C
Figure 4.20: Temperature set-point distribution for the block based pre-heatingmethod and the medium energy scenario. The one hour pre-heating block has a lower influence on the set-point temperaturesthan the slope-based pre-heating algorithm.
on average lower compared to the slope based pre-heating cases and similar or
slightly higher compared to the cases without pre-heating. Given the tempera-
ture set-points if the user is absent, the block based pre-heating method shows
more similarities to the cases without pre-heating than to the slope-based pre-
heating. Although the distribution is wider than in cases with no pre-heating,
indicating that some higher temperatures are set while the user is absent, the
99
4 Simulated Results
12
14
16
18
20
22
24Tsim if user present for all rooms but bathroom
CDM-N CPM-N CDM-S5 CPM-S5 CDM-B1 CPM-B1
12
14
16
18
20
22
24Tsim if user absent for all rooms but bathroom
Tem
per
atu
rein
◦ C
Figure 4.21: Distributions of the simulated temperatures for cases with andwithout pre-heating. The effects of the block based pre-heating arelower than the ones of the slope based pre-heating but still lead toan improved simulated temperature compared to the case withoutpre-heating.
median values are almost identical.
Relevant for the thermal comfort experienced by the user is of course the simu-
lated temperature; figure 4.21 shows the temperature distributions for the sim-
ulated temperature. For the block based pre-heating method the median values
of the simulated temperature are similar to the values of the base-case. There-
100
4.3 Adding pre-heating sequences
Table 4.5: Results for the CDM-B* cases
CDM-B1 CDM-B2 CDM-B3 CDM-B4
Qsim [kWh] 261.59 285.01 291.68 372.68
Qsim relative to RSM [%] 63 68 70 89
Qsim relative to CDM-N [%] 104 114 116 149
Daum criteria not met [h] 289.33 279.17 245.67 228.17
Thereof too cold [h] 289.33 279.17 245.67 228.17
Daum criteria relative toREM [%]
97 94 83 77
Daum criteria relative toCDM-N [%]
96 92 81 75
Total number of feedbacks 316 329 421 431
fore a similar low level of thermal comfort must be expected. This indicates
that a pre-heating period of one hour is probably too short for this building.
These conclusions are also supported by the results summarized in tables 4.5
and 4.6. They show the results depending on four different pre-heating times
for the different user types.
When compared, an increased pre-heating time also increases the energy de-
mand. In both behavioral models the increase in energy demand from three to
four hours is larger than the other increases but the effect is more pronounced
for the simulations with the Daum user. Each increase in temperature is linked
to an improved thermal comfort. In case of the PPD user the feedbacks also de-
crease whereas they increase for the Daum user cases. The reason is the same as
in the slope based case: The longer pre-heating time leads to higher simulated
temperatures. Therefore “too cold” feedbacks can less often be ignored by the
algorithm. Comparing the results with the slope based pre-heating the comfort
is always worse. Even if more or a comparable amount of energy is used (in
101
4 Simulated Results
Table 4.6: Results for the CPM-B* cases
CPM-B1 CPM-B2 CPM-B3 CPM-B4
Qsim [kWh] 253.35 279.49 305.43 341.85
Qsim relative to RSM [%] 61 67 73 82
Qsim relative to CPM-N [%] 103 113 124 139
PPD criteria not met [h] 295.33 266.17 240.33 206.00
Thereof too cold [h] 295.33 266.17 240.33 206.00
PPD criteria relative toREM [%]
107 96 87 74
PPD criteria relative toCPM-N [%]
90 81 73 63
Total number of feedbacks 130 104 93 86
both *-B4 cases), the block based pre-heating method cannot outperform the
slope based method.
Compared to the base case, the energy demand of the *-B1 cases is less than
5 % higher, while the time outside the comfort criteria is reduced by 5 % for
the Daum case and 10 % for the PPD case. This is in line with the simulated
temperatures shown in figure 4.21, which are close to those of the cases without
pre-heating.
Figures 4.22 and 4.23 show bar plots to compare the four different pre-heating
times with the RSM, REM, *-N and *-S5 cases. The difference in energy demand
is shown on the left, the difference when the comfort criteria is not met is shown
on the right. Negative values indicate a lower energy demand or less time out-
side the comfort criteria for the block based pre-heating method. In both cases
the block based pre-heating method is better than its counterpart if the values
are negativ. The energy demand increases with an increased pre-heating time.
This effect is clearly reflected in the results. Independent on the variants, the
102
4.3 Adding pre-heating sequences
RSM REMCDM-N
CDM-S5−200
−150
−100
−50
0
50
100
150
∆Q
inkW
h
RSM REMCDM-N
CDM-S5−100
−50
0
50
100
150
200
∆cr
iter
ian
otm
etin
h
1 hour 2 hours 3 hours 4 hours
Figure 4.22: Differences in energy consumption and comfort criteria for differ-ent pth parameters of the block based pre-heating method for theDaum user.
RSM REMCPM-N
CPM-S5−200
−150
−100
−50
0
50
100
∆Q
inkW
h
RSM REMCPM-N
CPM-S5−150−100−50
050
100150200250300
∆cr
iter
ian
otm
etin
h
1 hour 2 hours 3 hours 4 hours
Figure 4.23: Differences in energy consumption and comfort criteria for differ-ent pth parameters of the block based pre-heating method for thePPD user.
103
4 Simulated Results
higher energy demand due to longer pre-heating times directly translates into
higher comfort. Compared to the RSM case, the energy savings are between 50-
150 kWh/70-160 kWh (Daum/PPD), but also times outside the comfort criteria
are at least 100/200 hours more (Daum/PPD). For the REM case as well as the
*-N cases respectively, the thermal comfort is always improved independent of
the pre-heating duration, with one exception being the PPD-user with one hour
pre-heating time. Compared to the REM case, the energy demand is decreased
if the pre-heating time is below 4 h, and slightly increased for the 4 hour case.
Compared to the CDM-N/CPM-N case the energy demand is always higher.
The slope based pre-heating uses more energy with exception of the 4 hour
cases and comfort is better for all cases. Pre-heating shows an overall positive
effect on the thermal comfort compared to cases of intermittent heating with-
out pre-heating but also reduces the possible energy savings. Overall, the slope
based pre-heating method shows more promise. One reasonable explanation
of the different quality of the results is that the block based pre-heating time is
independent on the temperature difference. The slope based method will use
a longer pre-heating time if the difference is bigger due to the usage of a slope
while the block based method uses the same pre-heating time independent of
that difference. To the block based pre-heating’s advantage counts the fact that
the temperature set-point changes less often, which reduces battery consump-
tion, if a battery powered TRV is used.
4.3.3 Pre-heating with an adaptive estimation of parameters
The previous section has shown that pre-heating is a crucial method to ensure
thermal comfort. While the slope of 0.5 K/h was a good fit for the buidling un-
der test, the initial pre-heating duration of 1 h for the block based pre-heating
was not sufficient. Given the variety of buildings and heating systems, it can-
not be expected that one parameter fits all buildings. Therefore, section 3.1.3
on page 31 describes a method to dynamically change the pre-heating parame-
104
4.3 Adding pre-heating sequences
0.100.150.200.250.300.350.400.450.50
Slo
pe
inK
/h
Day 3Day 10
Day 17Day 24
Day 31
Daum
0100200300400500600
t ph
inm
inu
tes
Day 3Day 10
Day 17Day 24
Day 31Day 38
PPD
C*M-*A C*L-*A C*H-*A
Figure 4.24: Changes in slope and pre-heating time due to the adaptive pre-heating method. Medium and high energy demand require longerpre-heating times than the low energy demand case, independentof pre-heating method or behavioral model.
ters and minimum temperature. Although the results for the static pre-heating
methods are already promising, an adaptive approach may yield even better
results.
Depending on the pre-heating success of previous days, slope sph or time tph is
adapted, The effects are shown in figure 4.24. The first row shows the changes
105
4 Simulated Results
in the slope for the slope based pre-heating method, the second row shows the
changes in the pre-heating time for the block based pre-heating method. The
left column shows the results for the Daum user, the right column the results for
the PPD user. Each axes show the three different values for the high, medium
and low energy demand.
The slope for the low energy demand scenario shows a sharp drop at the be-
ginning of the simulations for both behavioral models but than recovers to its
default value after approximately two weeks. The Daum user shows a lower
minimum value compared to the PPD case. For the block based pre-heating
method the pre-heating time raises up to 6 h for the Daum case on day 16 and
declines to its initial value afterwards. For the PPD user the pre-heating time
raises for the first two weeks up to approximately 4.5 h and keeps this value un-
til day 33. After this it declines to its initial value. The behavior of pre-heating
parameters for the medium energy demand is different, especially for the Daum
user cases: At the beginning, the slope drops to its minimum value of 0.15 K/h
similar to sph in the low energy scenario. But it keeps this value much longer,
until day 33 with one minor increase at day 27. After day 33 it increase up to
0.45 K/h and keeps this value until the end of the simulation. For the PPD user
the value drops as fast as for the Daum user, but raises earlier, decreases again
and returns to its initial value eventually. Due to the upper limit of 0.5 K/h sph
could not increase further. Pre-heating times for the block based pre-heating
increase from the beginning up to a maximum of 10 h, showing only a small
decline down to 8 h towards the end of the simulation. For the PPD user the
changes are again less pronounced, hitting a maximum value of 6 h on day 17
and a decline to its initial value afterwards. The high energy scenario shows
pretty much the same behavior as the medium energy scenario, with the ex-
ception that it returns faster to its initial value for the CPH-SA case and for the
CPH-BA case the maximum pre-heating time is only 5 h but is kept higher for
a longer time, similarly to the low energy demand scenario. Generally the low
energy scenario seems to require shorter pre-heating times compared to the
106
4.3 Adding pre-heating sequences
81012141618202224
Tp
h,m
inin
◦ C
Day 3Day 10
Day 17Day 24
Day 31
Daum
810121416182022
Tp
h,m
inin
◦ C
Day 3Day 10
Day 17Day 24
Day 31Day 38
PPD
C*M-*A C*L-*A C*H-*A
Figure 4.25: Changes in the minimum temperature for the living room. Againthe low energy demand shows lower minimum temperatures ascompared to the other cases. For the Daum user with the blockbased pre-heating the algorithm even ends in continuous heating.
other scenarios. Also the higher preferred temperature of the Daum user leads
to longer pre-heating times in any energy scenario.
In contrast to the globally calculated pre-heating time, the minimum temper-
ature is calculated separately for each room. Although the resulting minimum
temperature is different for each room, the overall behavior is similar therefore
107
4 Simulated Results
only the results for the living room are shown in figure 4.25. The layout is the
same as in figure 4.24, showing the slope based pre-heating method on top and
the block based at the bottom. The Daum user is on the left, the PPD user on
the right. For the low energy demand scenario with slope based pre-heating,
Tph, min is raised to 13 ◦C in the first two weeks for the Daum user and not at all
for the PPD user. The block based pre-heating scenario shows an increase up
to 21 ◦C on day 14 but a fast decline to its initial value afterwards. The increase
in Tph, min is lower for the PPD user, just up to 19 ◦C, but the decline afterwards
takes longer. For all cases but the CDM-SA case, the medium and high energy
scenario show the same behavior: Tph, min increases up to 17 ◦C for the slope
based pre-heating in the first few days, starts declining around day 16 and re-
turns to its initial value at day 26. For the block based pre-heating method,
Tph, min increases up to 22 ◦C for the Daum user, which corresponds to constant
room heating. This high value is kept until the end of the simulation. Again the
increase in Tph, min is less severe for the PPD user, reaching a maximum value of
19 ◦C after a week. Both cases return to their initial value eventually. Medium
and high energy scenario show a difference for the CDM-SA case. The medium
energy scenario’s Tph, min increases up to 18 ◦C and remains at an elevated level
close until the end of the scenario, while Tph, min is never increased for the high
energy scenario.
As the development of Tph, min is correlated to sph or tph respectively, the gen-
eral conclusion is the same: The low energy scenario works with lower values
for Tph, min, compared to the other energy scenarios and the average Tph, min is
lower for the PPD user than the Daum user. Lowering Tph, min and shortening
the pre-heating time towards the end of the simulation is in most cases possi-
ble because the average outside temperature and the global radiation increase
at the end of the simulated period, making reheating rooms easier for the heat-
ing system.
The temperature set-points for a present and absent user are shown in fig-
ure 4.26. Shown are the slope based pre-heating method with a fixed slope of
108
4.3 Adding pre-heating sequences
101214161820222426
Tset if user present for all rooms but bedroom
CDM-S5
CDM-B1
CDM-B4
CDM-SA
CDM-BA
CPM-S5
CPM-B1
CPM-B4
CPM-SA
CPM-BA
101214161820222426
Tset if user absent for all rooms but bedroom
Figure 4.26: Comparison of the temperature set-points for the different pre-heating methods. Both adaptive pre-heating methods show higherset-point temperatures on absence.
0.5 K/h and the adaptive one, additionally the block based pre-heating method
for one and four hours and with an adaptive evaluation of the pre-heating du-
ration. Results are shown for both user types. The upper figure with the user
present shows for the Daum user that the median temperature set-points are
similar for the slope-based pre-heating methods and the adaptive block based
pre-heating. The fixed duration method shows on average a 1 K lower set-point.
109
4 Simulated Results
For the PPD user the temperature set-point is comparable for every pre-heating
method except the adaptive slope based pre-heating method, whose median
value is approximately 1 K lower. Compared to the other distributions, the tem-
perature distribution of the *-B1 case is more narrow but overall the set-points
do not show notable differences within one user characteristic. The median
temperature set-points of the Daum user are higher than the PPD ones.
More notable differences can be found if the user is absent. For both user char-
acteristics the temperature distribution of the *-B1 and *-B4 cases is wider. The
*-B1 case shows the lowest median value, far below any other case. All other
cases show comparable median values within the same user characteristics.
The adaptive pre-heating versions show a slightly higher median value, indi-
cating that the adaptive pre-heating will usually use longer pre-heating than
the static cases. Comparing the different behavioral models shows that tem-
perature set-points are higher for the Daum user.
Effects of the different pre-heating methods on the simulated air temperature
can be seen in figure 4.27. For both users the median simulated temperature
is lowest for the *-B1 pre-heating method. The results are below the comfort
range of both user characteristics. The *-B4 case shows the second lowest me-
dian temperature and it is at the lower boundary for the PPD user and below the
comfort threshold for the Daum user. For the PPD user the simulated tempera-
tures are similar for both slope based algorithms. For the Daum user the adap-
tive method achieves a slightly higher median temperature that coincides bet-
ter with the Daum user’s preferred temperature. The *-BA cases show slightly
higher median simulated temperatures but the distribution is wider and there-
fore includes also simulated temperatures lower than in the *-SA cases which
may be less comfortable for the user. If the user is absent, the distributions are
similar to the set-points shown in figure 4.26 but with a narrower distribution
due to the buildings thermal inertia.
The effects of the adaptive pre-heating time and minimum temperature are
110
4.3 Adding pre-heating sequences
12
14
16
18
20
22
24
26Tsim if user present for all rooms but bedroom
CDM-S5
CDM-B1
CDM-B4
CDM-SA
CDM-BA
CPM-S5
CPM-B1
CPM-B4
CPM-SA
CPM-BA
12
14
16
18
20
22
24
26Tsim if user absent for all rooms but bedroom
Figure 4.27: Comparison of the simulated temperatures for the different pre-heating methods. The adaptive pre-heating methods lead, espe-cially for the Daum user, to higher simulated temperatures if theuser is present, improving his thermal comfort.
summarized in table 4.7 for the medium energy scenario. With regards to en-
ergy demand, the slope based pre-heating method uses approximately 6 % less
energy for Daum user and 3 % more for the PPD user than the block based
pre-heating method. In terms of comfort criteria, the slope based pre-heating
method shows better results. The comfort criteria is approximately 10 % less of-
ten violated for the PPD user and 50 % less for the Daum user. The slope based
111
4 Simulated Results
Table 4.7: Results of the dynamic pre-heating methods for the medium energyscenario
CDM-SA CPM-SA CDM-BA CPM-BA
Qsim [kWh] 380.17 366.27 405.86 357.59
Qsim relative to RSM [%] 91 88 97 85
PPD criteria not met [h] - 69.00 - 77.83
Thereof too cold [h] - 69.00 - 77.83
Daum criteria not met [h] 111.17 - 218.00 -
Thereof too cold [h] 87.83 - 102.67 -
PPD criteria relative toREM [%]
- 25 - 28
Daum criteria relative toREM [%]
37 - 73 -
Total number of feedbacks 452 47 487 40
algorithm needs less feedback in case of the Daum user but more feedbacks
with regard to the PPD user. In contrast to the previous simulations, the com-
fort criteria is not only violated due to a temperature being too cold but also
because of a temperature being too warm. The reasons for this will be ana-
lyzed later on. Also, in the adaptive case, the slope based pre-heating method
is preferable to the block based pre-heating method.
4.4 Comparison of the di�erent pre-heating measures
A comparison between the different versions of the algorithms and their in-
fluence on the use of energy and the meeting of the user comfort is shown in
figure 4.28 for the Daum user and in figure 4.29 for the PPD user. These figures
show the energy demand and the comfort relative to the reference cases. The
112
4.4 Comparison of the different pre-heating measures
0.0 0.2 0.4 0.6 0.8 1.0Relative energy consumption [lower is better]
0.0
0.2
0.4
0.6
0.8
1.0
Rel
ativ
e am
ount
of d
isco
mfo
rt [l
ower
is b
ette
r]
CDM-N
CDM-S5
CDM-B1CDM-B2CDM-B3
CDM-B4
CDM-SA
CDM-BA
RSM
REM
Figure 4.28: Comparison of the results of the different algorithm versions withrespect to energy, comfort and number of feedbacks for the Daumuser. Both slope-based pre-heating algorithms show the bestresults.
energy demand is normalized to the energy demand of the RSM case and the
thermal comfort is normalized to the comfort of the REM case. As discussed in
section 3.2.5 the RSM case should be an upper limit to the energy demand while
the REM case should be an upper bound on the time the comfort criteria is vi-
olated. This upper bounds are marked with a red, dashed line. The area where
both upper limits are violated is marked red. Additional to the position of the
markers the size of the marker is also relevant. The larger the marker, the more
feedbacks have been given3. Therefore a good working algorithm should be in
the lower left corner with a small marker. To improve faster distinction, simu-
lations without any pre-heating method use a grey marker, a static pre-heating
is marked red and an adaptive pre-heating orange.
The base case without any pre-heating shows a thermal comfort comparable
3Due to the high difference in feedbacks between the Daum and PPD user, the markers do not usethe same scaling factor in the different figures.
113
4 Simulated Results
to the REM case while also reducing the energy demand (down to ≈ 0.6 from
≈ 0.8). Adding the block based pre-heating method improves thermal comfort,
slightly decreases the number of feedbacks and increases the energy demand.
Longer pre-heating times result in better comfort and higher energy demand,
but increasing the pre-heating time from three to four hours has only a small ef-
fect on thermal comfort but a large one on energy consumption. The adaptive
version of the block based pre-heating method results in comparable thermal
comfort to the static cases with three and four hours of pre-heating, a com-
parable amount of feedbacks but an higher energy demand. Given the high
minimum temperature Tph, min shown in figure 4.25 this must have been ex-
pected. The slope based pre-heating method shows a better thermal comfort
rating than each of the block based pre-heating cases, including the adaptive
one. Its energy demand lies well between the three and four hours version of
the previous cases. With a higher rate of thermal comfort and simultaneously
lower energy demand, the static version of the slope based pre-heating method
outperforms all of the cases using the block based pre-heating method. The
adaptive version of the slope based pre-heating method shows the best thermal
comfort. But these results come at the cost of a higher energy demand, reduc-
ing the possible energy savings to approximately 8 % compared the the RSM
case while providing a better level of comfort. Given the low level of thermal
comfort for the REM case, the results of the block based pre-heating method
should be considered as not sufficient. The slope based pre-heating shows ac-
ceptable results with regards to thermal comfort and good results considering
the energy demand.
Figure 4.29 shows the results for the PPD user. The base case without pre-heating
shows the lowest energy demand, but the thermal comfort is even worse than
in the energy efficient case and the number of necessary feedbacks very high.
The static block based pre-heating method continuously improves the thermal
comfort with increased pre-heating duration and increases energy demand.
The static version of the slope based pre-heating method shows again a signifi-
114
4.4 Comparison of the different pre-heating measures
0.0 0.2 0.4 0.6 0.8 1.0Relative energy consumption [lower is better]
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Rel
ativ
e am
ount
of d
isco
mfo
rt [l
ower
is b
ette
r]
CPM-N
CPM-S5
CPM-B1CPM-B2
CPM-B3CPM-B4
CPM-SA CPM-BA
RSM
REM
Figure 4.29: Comparison of the results of the different algorithm versions withrespect to energy, comfort and number of feedbacks for the PPDuser. The adaptive and the slope-based pre-heating method showthe best result.
cantly better thermal comfort than any of the static versions of the block based
pre-heating method, reducing the relative thermal comfort from 0.8 to 0.4 com-
pared to CPM-B4, while requiring the same amount of energy. The adaptive
versions of both pre-heating methods show a comparable energy demand and
comfort, both outperforming their static versions and saving approximately
15 % energy compared to the RSM case. Both adaptive pre-heating methods
require significantly fewer feedbacks than the static cases.
The results for the low and high energy demand scenarios can be found in
figures A.1-A.4 in the appendix. While they may differ in the details, they all
show the same tendency that the slope based pre-heating method outperforms
the block-based one and that the adaptive versions show better results than
the static ones. Using the dynamic pre-heating methods, the slope based pre-
heating algorithm results in very good comfort values, 0.3 for the Daum user
and 0.2 for the PPD user. while the energy savings are in the range of 10-18 %
115
4 Simulated Results
compared to the RS* cases. The block based pre-heating method results in still
good comfort values for the PPD user but fails with the Daum user, showing
worse results than static cases. It also uses more energy in 3 out of the 4 cases.
This section has shown that a form of pre-heating is necessary to ensure ther-
mal comfort and that the adaptive pre-heating approach should take prece-
dence over the static approach. Given the overall results, the adaptive version
of the slope based pre-heating method is chosen as a best fit for the algorithm
and used further on.
4.5 E�ect of an increased heating power on the
algorithms performance
The previous section has shown that the heating system is not capable to supply
the higher room temperatures required to satisfy the Daum user in the medium
and high energy demand scenario. The heating system of the apartment was
designed according to standards that assume a room temperature of 20 ◦C when
calculating the necessary heating power. It is therefore not surprising that a
preferred temperature of 23 ◦C and intermittent heating is challenging for the
heating system. In this section a more powerful heating system is tested. The
“+H” suffix denotes simulations that use the higher powered heating system.
The chosen method to increase the heating system’s power was an increase of
its flow temperature. This increases the maximum power that can be supplied
by the radiator without changing the hydraulic system. A method that would
also be feasible in an existing building without the need for any changes in the
physical heating system.
Figure 4.30 shows the effect on sph and tph, figure 4.31 the effect on Tph, min for
the living room but the results are exemplary for every other room. The fig-
ures use the same layout as before however only values for the medium energy
scenario are shown. In the slope based scenario, sph behaves similarly to the
116
4.5 Effect of an increased heating power on the algorithms performance
0.100.150.200.250.300.350.400.450.50
Slo
pe
inK
/h
Day 3Day 10
Day 17Day 24
Day 31
Daum
0100200300400500600
t ph
inm
inu
tes
Day 3Day 10
Day 17Day 24
Day 31Day 38
PPD
C*M-*A C*M-*A+H
Figure 4.30: Differences in slope and pre-heating time between standard andhigher powered case. Due to the higher powered system pre-heating times are reduced independent of pre-heating method andbehavioral model.
basic scenario for the first two weeks, afterwards sph rises fast to its maximum
value and remains mostly at this value. In the block based pre-heating method,
tph raises at the same speed as in the base case but the maximum value for tph
reaches only approximately 5 h for the Daum user and 3.5 h for the PPD user
while the basic scenario rises up to 10 h or 6.5 h respectively. For the Daum user
the pre-heating time remains constantly below the basic scenario while the val-
117
4 Simulated Results
81012141618202224
Tp
h,m
inin
◦ C
Day 3Day 10
Day 17Day 24
Day 31
Daum
810121416182022
Tp
h,m
inin
◦ C
Day 3Day 10
Day 17Day 24
Day 31Day 38
PPD
C*M-*A C*M-*A+H
Figure 4.31: Differences in Tph, min between the standard and the higher pow-ered case for the living room. The higher powered heating systemallows for far lower minimum temperatures.
ues of tph converge for the PPD user towards the end of the simulation time.
Using a more powerful heating system allows the algorithm to use a steeper
slope sph or shorter tph. Especially in combination with the Daum user the
pre-heating time is significantly reduced. Without the upper limit for sph the
pre-heating time could be even shorter.
Figure 4.31 shows that not only the pre-heating times are reduced but also the
118
4.5 Effect of an increased heating power on the algorithms performance
Table 4.8: Comparison of the results of the basic heating and the higher pow-ered heating system for the medium energy scenario and the Daumuser
CDM-SA CDM-SA+H
CDM-BA CDM-BA+H
Qsim [kWh] 380.17 364.76 405.86 363.46
Qsim relative to RSM [%] 91 87 97 87
Daum criteria not met [h] 111.17 62.50 218.00 130.50
Thereof too cold [h] 87.83 34.67 102.67 108.67
Daum criteria relative toREM [%]
37 21 73 44
Total number of feedbacks 452 474 487 461
minimum Temperature Tph, min is lower. For the slope based pre-heating method
Tph, min is never increased but reduced to its minimum value of 10 ◦C within the
first days of the simulation. This is a notable difference compared to the sim-
ulation with the less powerful heating system that reached values up to 18 ◦C
for Tph, min for several days and only decreased in the last third of the simula-
tion time. In the block based pre-heating method, Tph, min increases up to 18 ◦C
only to constantly decreases to its minimum value. The less powerful heating
system reaches similar maximum values for the PPD user but keeps these val-
ues for a longer period of time. For the Daum user, Tph, min raises up to 22 ◦C
and does not decrease until the end of the simulation time. This is combined
in both cases with higher values for tph. As previous analysis has shown, the
standard heating system is, although sized according to standards, not power-
ful enough for intermittent heating and the Daum user. Therefore it is reason-
able to assume that the higher powered heating system is far better in matching
the heating demand.
The tabulated results are shown in table 4.8 for the Daum user. For both pre-
119
4 Simulated Results
heating methods, the energy demand of the higher powered heating system
is lower, although it is theoretically more powerful. In the slope based algo-
rithm the energy demand is decreased by 5 %, in the block based the reduction
is approximately 10 %. The decreased energy demand can be explained by the
shorter pre-heating times and the lower values for Tph, min. The higher pow-
ered heating system also positively impacts the comfort criteria, reducing the
times the comfort criteria is not met by approximately 40 % for both behav-
ioral models. For the block based pre-heating this improvement is realized by
a shorter time rooms are overheated. The time rooms are too cold is slightly
increased from 103 h to 108 h. With the adaptive algorithms the Daum user ex-
periences not only rooms being too cold but also ones being too warm. In case
of the slope based algorithm, the room was 23 h too warm with the basic heat-
ing system, compared to 28 h for the higher powered system. More significant
is the difference for the block based pre-heating method: While the rooms were
overheated for 115 h in the basic scenario, this value was reduced to 22 h in the
higher powered scenario, due to the shorter pre-heating times.
This behavior can be explained by referring to figure 4.32, which shows over-
or underheated rooms for the Daum user. The vast majority of overheating
events takes place in the bedroom, a negligible amount in the living room.
Other rooms are unaffected by overheating. The bedroom is often overheated
because pre-heating the bedroom for the morning starts already at night when
lower temperatures are desired. The effect is far more pronounced for the block
based pre-heating method because it changes to a high temperature set-point
hours before it is needed whereas the slope based pre-heating method uses a
slope. But as explained in section 3.2.5, a different consideration of the bed-
room is necessary and therefore the overheating in the bedroom should not
be of too much concern. Focusing on the rooms of higher importance, like
kitchen and living room, it is evident that the higher powered heating system
impacts the block based pre-heating method only slightly whereas the results
of the slope based pre-heating method improve by a large amount. Indepen-
120
4.5 Effect of an increased heating power on the algorithms performance
Bath Bedroom Kitchen Living Room Study0
20
40
60
80
100
120
Tim
e of
low
com
fort
in h
too coldtoo warm
CDM-SA CDM-SA+H CDM-BA CDM-BA+H
Figure 4.32: Comparison of times of low comfort for the Daum user for the nor-mal and higher powered heating system. The higher powered heat-ing system may overheat the bedroom due to an early start of thepre-heating phase. Especially for the slope based pre-heating thehigher powered system is an advantage.
dent of the pre-heating method the higher powered heating system shows a
positive effect on the user’s comfort level.
Table 4.9 shows the results for the PPD user which are similar to the Daum user:
The higher powered heating system needs less energy although the savings are
smaller: 2 % for the slope based and 4 % for the block based algorithm. Thermal
comfort is positively impacted for the slope-based algorithm, times of comfort
criteria violation are 12 % lower. For the block based pre-heating method ther-
mal comfort is decreased by 20 % for the higher powered heating system. No
overheating is experienced with the PPD user.
Therefore, it can be concluded that a more powerful heating system cannot only
improve the user’s thermal comfort but also reduce the total energy demand by
shortening the necessary pre-heating times and reducing the minimum tem-
peratures. Independent of the heating system, the slope based pre-heating al-
121
4 Simulated Results
Table 4.9: Comparison of the results for the basic heating and the higher pow-ered heating system for the medium energy scenario and PPD user
CPM-SA CPM-SA+H
CPM-BA CPM-BA+H
Qsim [kWh] 366.27 358.39 357.59 345.13
Qsim relative to RSM [%] 88 86 85 82
PPD criteria not met [h] 69.00 59.33 77.83 92.50
Thereof too cold [h] 69.00 59.33 77.83 92.50
PPD criteria relative toREM [%]
25 21 28 33
Total number of feedbacks 47 49 40 63
gorithm outperforms the block based algorithm.
4.6 E�ects of an increased radiative ratio
In section 3.2.4 a faster heating system with a higher radiative ratio was sug-
gested as a better suited heating system for intermittent heating. Compared to
the standard system the power of the fast, radiative heater is slightly increased
and the radiative ratio changes from 30 % to 70 %. Simulations using the fast,
radiative heater are labeled with an “+R”.
Figure 4.33 shows the differences in heat supply to the room given the different
heating systems. This figure shows heating in the living room for January, 30th
to January, 31st. Data are taken from the CDM-SA and CDM-SA+R cases. The
CDM-SA case shows a constant energy supply to the room due to the rather
flat slope of 0.16 K/h of the pre-heating curve at this day. The maximum power
delivered to the room does not exceed 800 W for the CDM-SA case, well be-
low its nominal power. The nominal power may not be available to the system,
122
4.6 Effects of an increased radiative ratio
00:0030-Jan2007
00:0031-Jan
06:00 12:00 18:00 06:00 12:00 18:000
200
400
600
800
1000
1200
1400
Hea
tin
gPo
wer
inW
CDM-SA CDM-SA+R
Figure 4.33: Heating Power of the standard heating system and the fast, ra-diative heater. While the standard heater continuously heats theroom, due to long pre-heating phases, the fast radiative heatersupplies energy only if necessary.
because the flow temperature is lower than the one used for calculating the sys-
tem’s nominal power. In contrast, the fast radiative heater shows times without
heating and increases its heat output at approximately 11:00 h for the first time.
The power output is kept on its maximum level of 1 300 W for several hours, af-
terwards the power declines fast. At the same time also the standard heating
system lowers its power output, indicating that the expected user presence is
over.
Figure 4.34 shows the effect of the different heating systems on the air and sur-
face temperature of the living room for the same time period as figure 4.33.
Shown are the results for the standard case, the heating system with an in-
creased power but still using a standard radiator and the system with higher
radiative ratio. The air temperature of the radiator based simulations is much
more dynamic than its surface temperature. In the morning, the air tempera-
123
4 Simulated Results
19
20
21
22
23
24
Tair
Tair, +H
Tair, +R
00:0030-Jan2007
00:0031-Jan
06:00 12:00 18:00 06:00 12:00 18:0019
20
21
22
23
24
Tsurf
Tsurf, +H
Tsurf, +R
Figure 4.34: Air and surface temperatures of the rooms depending on the heat-ing system. The higher powered heating system and the fast, ra-diative heater can both increase the air temperature further thanthe standard system. But only the fast, radiative heater is able toimpact the surface temperature.
ture of the *+H case is increased from 19.5 to 21.5 ◦C clearly showing a slope
based pre-heating slope, whereas the surface temperature just changes by ap-
proximately 0.5 K in the same time period. The fast, radiative heater couples
surface temperature with air temperature, both showing a rather similar be-
havior. As thermal comfort normally depends on a mixture of air and surface
124
4.6 Effects of an increased radiative ratio
0.0
0.1
0.2
0.3
0.4
0.5
Slo
pe
inK
/h
Day 3Day 10
Day 17Day 24
Day 31
Daum
0100200300400500600
t ph
inm
inu
tes
Day 3Day 10
Day 17Day 24
Day 31Day 38
PPD
C*M-*A C*M-*A+R
Figure 4.35: Dyamic pre-heating parameters for the fast, radiative heater. Thefast, radiative heater allows for shorter pre-heating times, espe-cially for the Daum user.
temperature4 this is a more desired behavior. On January, 31st 11:00 all surface
temperatures raise in a similar manner. But this temperature increase is not
due to an increased power output of the heating systems (compare figure 4.33
which show a rather low heat output at this time) but to solar radiation, which
explains why the rooms behave similarly.
4In contrast to the Daum user whose model just uses air temperature.
125
4 Simulated Results
Figure 4.35 shows the development of the dynamic pre-heating parameters. Com-
pared to the standard heating system, the pre-heating duration is much shorter.
For the slope-based pre-heating method the value for sph, min drops to its min-
imum value of 0.15 K/h but then returns to its maximum value of 0.5 K/h at
day 22 for the Daum user. In comparison, the standard heating system is most
of the time at its lower limit. For the PPD user the differences are less pro-
nounced but most of the time sph, min is on a higher value for fast, radiative
heater. For the block based pre-heating method the maximum value of tph, min
is reduced by approximately 50 % compared to the standard case for the Daum
user and by 25 % for the PPD user. Again, differences for the PPD user are
smaller than for the Daum user.
The development of the minimum temperature Tmin, ph for the living room is
shown in figure 4.36, results for the other rooms are comparable. For both slope-
based cases, Tmin, ph raises by 2 K at the beginning but returns fast to its mini-
mum value of 10 ◦C. Tmin, ph is higher for the standard heating case with a max-
imum value of 18 ◦C. In the CDM-BA+R case, Tmin, ph reaches a maximum value
of 20 ◦C shortly after starting but than continuously returns to its initial value. In
contrast, the CDM-BA case reaches a maximum temperature of approximately
22 ◦C without lowering this value until the end of the simulation, which corre-
sponds to constant room heating. In the CPM-BA case, Tmin, ph is increased up
to 19 ◦C, keeps this value for several days and than returns to its initial value
around day 24. It shows a behavior very similar to the standard heating system
results.
With shorter pre-heating times and lower minimum temperatures a reduced
energy demand may be expected. Tables 4.10 and 4.11 summarize the results
of the simulations. For the Daum user the energy savings result in approxi-
mately 13 % for both pre-heating methods. Simultaneously, the user comfort is
increased. For the slope based algorithm the total time the Daum criteria is not
met is reduced by 40 % while the duration the room is perceived as too cold is
reduced by 22 %. For the block based algorithm the results are slightly better:
126
4.6 Effects of an increased radiative ratio
81012141618202224
Tp
h,m
inin
◦ C
Day 3Day 10
Day 17Day 24
Day 31
Daum
810121416182022
Tp
h,m
inin
◦ C
Day 3Day 10
Day 17Day 24
Day 31Day 38
PPD
C*M-*A C*M-*A+R
Figure 4.36: Development for the minimum temperature for the fast, radiativeheater. Top shows the slope based pre-heating method, bottomshows the block based one.
Due to long pre-heating times in the CDM-BA case the bedroom was often per-
ceived as too warm (for reference, compare figure 4.32). This is now improved
and the overheating time is down from 115 h to 1 h. Low thermal comfort be-
cause of cold rooms remains with approximately 100 h virtually the same. For
the PPD user the results differ slightly . The slope-based algorithm manages
again to use less energy (approximately 5 %) but this time thermal comfort is
127
4 Simulated Results
Table 4.10: Results for the fast, radiative heater and Daum user
CDM-SA CDM-SA+R
CDM-BA
CDM-BA+R
Qsim [kWh] 380.17 342.93 405.86 359.15
Qsim relative to RSM [%] 91 82 97 86
Daum criteria not met [h] 111.17 71.33 218.00 102.67
Thereof too cold [h] 87.83 69.17 102.67 101.83
Daum criteria relative toREM [%]
37 24 73 35
Total number of feedbacks 452 477 487 460
Table 4.11: Results for the fast, radiative heater and PPD user
CPM-SA CPM-SA+R
CPM-BA CPM-BA+R
Qsim [kWh] 366.27 348.57 357.59 366.31
Qsim relative to RSM [%] 88 83 85 88
PPD criteria not met [h] 69.00 74.17 77.83 90.17
Thereof too cold [h] 69.00 74.17 77.83 90.17
PPD criteria relative to REM[%]
25 27 28 33
Total number of feedbacks 47 48 40 54
slightly worse. The block based pre-heating method results are even worse: En-
ergy demand is increased by about 2 % but also thermal comfort is worse by
5 %.
Overall the system with its lower latency and a higher radiative ratio leads to
energy savings and may improve the thermal comfort. Pre-heating times could
128
4.7 Effects of different learning methods
Day 1Day 11
Day 21Day 31
Day 41
CDM-SA
0
5
10
15
20
25
30
Day 1Day 11
Day 21Day 31
Day 41
DDM-SADay 1
Day 11Day 21
Day 31Day 41
WDM-SA
Num
ber o
f fee
dbac
ks
Number of valid too cold feedbacks per Day
Figure 4.37: Valid Feedbacks over time for the Daum user in the *DM-SA cases.Cluster and Daily learning show similar results, weekly learning re-quires more feedbacks.
be reduced significantly.
4.7 E�ects of di�erent learning methods
As explained in section 3.1.3 three different learning methods were tested al-
though the daily and weekly learning algorithm may be considered as two spe-
cial cases of the more general case of clustered learning. The daily learning
equates to a clustered learning with only one cluster and weekly learning can
be described as a clustered learning approach with seven distinct clusters. In
this chapter, results are shown for the adaptive version of the slope based pre-
heating method as the previous sections have shown that this combination
yields the best results.
The main effect of the different learning algorithms should expectedly be on
the feedbacks given by the user. The development of the user feedbacks is given
129
4 Simulated Results
Day 1Day 11
Day 21Day 31
Day 41
CPM-SA
0
5
10
15
20
Day 1Day 11
Day 21Day 31
Day 41
DPM-SADay 1
Day 11Day 21
Day 31Day 41
WPM-SA
Num
ber o
f fee
dbac
ksNumber of valid too cold feedbacks per Day
Figure 4.38: Valid Feedbacks over time for the PPD user in the *PM-SA cases.Cluster and Daily learning require a similar amount of feedbacks,the weekly algorithm requires more feedbacks.
in figures 4.37 and 4.38 for the Daum and the PPD user respectively, with only
valid too cold feedbacks being shown. For the Daum user, clustered and daily
learning show a similar behavior while the weekly algorithm shows a higher
number of feedbacks at the end of the simulation. The same holds true for the
PPD user where the weekly learning seems to require more feedbacks towards
the end of the simulation. This is due to the fact that the overall learning of
the weekly algorithm is slower, as described in section 3.1.3. The theoretical
advantages of the clustered learning compared with the daily learning should
be evident between days that do not show the same user behavior. In case of
the simulated user, different behavior is expected between Friday to Saturday
and Sunday to Monday. Therefore, daily learning should show more feedbacks
on Saturdays and Mondays compared to the clustered learning. These effects
cannot be seen in figures 4.37 and 4.38, because the long pre-heating times have
a high impact on the necessary feedbacks.
Results for the different learning methods are summarized in tables 4.12 and
130
4.7 Effects of different learning methods
Table 4.12: Results of the different learning methods for the *DM-SA cases
CDM-SA DDM-SA WDM-SA
Qsim [kWh] 380.17 382.84 347.75
Qsim relative to RSM [%] 91 91 83
Daum criteria not met [h] 111.17 130.83 125.67
Thereof too cold [h] 87.83 83.17 125.67
Daum criteria relative to REM [%] 37 44 42
Total number of feedbacks 452 473 497
Table 4.13: Results of the different learning methods for the *PM-SA cases
CPM-SA DPM-SA WPM-SA
Qsim [kWh] 366.27 376.89 352.91
Qsim relative to RSM [%] 88 90 84
PPD criteria not met [h] 69.00 50.83 117.00
Thereof too cold [h] 69.00 50.83 117.00
PPD criteria relative to REM [%] 25 18 42
Total number of feedbacks 47 47 75
4.13 for the Daum and PPD user, additional results for the low and high energy
demand can be found in tables A.9-A.12. With respect to energy demand, the
weekly learning uses less energy than the other two learning methods, usually
in the range of 5-10 %. The differences between clustered and daily learning are
usually smaller with the clustered learning using less energy. With regards to
the comfort criteria, the clustered learning slightly outperforms the daily learn-
ing. If only violations of the comfort criteria are measured that are too cold, the
situation changes: the daily learning outperforms the clustered learning. With
respect to thermal comfort,the weekly learning is always inferior compared to
131
4 Simulated Results
the other learning methods. Regarding feedbacks, clustered and daily learning
show similar results, weekly learning is again inferior. The energy savings of
the weekly learning go along with reduced thermal comfort, making the weekly
learning method a less reasonable choice. The theoretical advantages of the
clustered learning with respect to feedbacks and comfort do not show com-
pared to the daily learning in practical use.
Overall, daily and clustered learning show comparable results regarding feed-
backs and thermal comfort. With regards to energy demand, clustered learning
is slightly advantageous. Combined with the theoretical advantages of the clus-
tered learning, it should be used as the preferred learning method.
4.8 E�ects of di�erent insulation standards
As mentioned in section 3.2.3 the simulations have been performed for three
different insulation models though in most cases only the medium demand
scenario has been shown. Due to the differences in heating demand the re-
sults have been different especially in situations where the heating system faced
problems in providing the necessary heating power. The ratio of available heat-
ing power to heating demand seems to influence the duration of pre-heating
periods and may even impact energy demand. Therefore, a higher powered sys-
tem may result in an overall lower energy demand. For an overall comparison
the results of the different pre-heating methods are summarized in tables 4.14
and 4.15 for the Daum and the PPD user.
For the slope based pre-heating methods (independent of the user type), the
comfort criteria is best met for the low energy scenario, followed by the high
energy scenario and lastly the medium energy scenario. This is the same order
as the ratio available heating power to heating demand. The low energy sce-
nario has the highest ratio, the medium energy demand the lowest. This is also
true for the Daum user and the block based pre-heating method if only com-
132
4.8 Effects of different insulation standards
Table 4.14: Results for the CD*-*A cases
CDL-SA
CDM-SA
CDH-SA
CDL-BA
CDM-BA
CDH-BA
Qsim [kWh] 250.74 380.17 390.17 267.82 405.86 443.10
Qsim relative toRS* [%]
89 91 88 96 97 100
Daum criteria notmet [h]
79.00 111.17 110.50 280.83 218.00 230.50
Thereof too cold[h]
28.17 87.83 72.00 77.00 102.67 83.83
Daum criteria rel-ative to RE* [%]
27 37 37 95 73 78
Total number offeedbacks
462 452 495 555 487 482
Table 4.15: Results for the CP*-*A cases
CPL-SA
CPM-SA
CPH-SA
CPL-BA
CPM-BA
CPH-BA
Qsim [kWh] 235.88 366.27 363.95 213.03 357.59 388.13
Qsim relative toRS* [%]
89 91 88 96 97 100
PPD criteria notmet [h]
38.33 69.00 48.33 92.00 77.83 94.33
Thereof too cold[h]
38.33 69.00 48.33 92.00 77.83 94.33
PPD criteria rela-tive to RE* [%]
17 25 19 40 28 26
Total number offeedbacks
38 47 44 53 40 58
133
4 Simulated Results
Table 4.16: Relative energy savings compared to the RS* cases
slope based block based
scenario Daum PPD Daum PPD
low -10 % -16 % -4 % -24 %
medium -9 % -12 % -3 % -14 %
high -12 % -18 % 0 % -13 %
fort violations are counted that are too cold (ignoring the too warm violations
due to an overheated bedroom). The PPD user has a different order in any case.
Possible energy savings compared to the standard behavior are summarized in
table 4.16. In the slope-based pre-heating algorithm, the relative energy sav-
ings are highest for the high energy scenario, followed by the low energy sce-
nario and ended with medium energy scenario. In the block based scenario,
the order is low, medium, high. A correlation to the ratio between available
heating power to heating demand is not obvious. The differences in savings be-
tween the medium and high energy scenario confirm though that a low ratio
may result in higher energy demand. Independent from the insulation stan-
dard, the adaptive algorithm allows for energy savings in the range of 10-20 %
for the slope based pre-heating algorithm with acceptable comfort conditions.
4.9 Independence from initialization
The previous sections have covered the different methods that were used to
improve the thermal comfort and reduce the energy demand. The following
two sections focus on the independence from the initialization and user types
to show that the proposed algorithm works in a general way and is not limited
to a special configuration.
Many parts of this work rely on random numbers to create a non-deterministic
134
4.9 Independence from initialization
behavior. Random numbers are not only used to decide how the algorithm acts,
but also how and if the user gives feedback to the system. To make sure that the
results are repeatable and comparable, all simulations were conducted using
the same initialization numbers for the random number generator. It is now
necessary to show that the algorithm yields comparable results using different
sets of random numbers. Comparing the results of differently initialized simu-
lations may indicate if the algorithm behaves comparable, but a direct compar-
ison of the time series will be more meaningful. Although the advanced algo-
rithms return better results than the base case, only the base cases were studied,
because only this part is stochastic. The other methods are deterministic and
would only interfere with the comparison.
A not correlated time-series was defined as benchmark. It can be compared
to the time-series created by the algorithm. The algorithm-based time-series
should all show a similar behavior while the non correlated time-series should
show a different one. The uncorrelated time-series have to use the same tem-
perature range, but temperatures are chosen randomly for each timestep. If the
living room’s temperature set-points are in the range of 14 ◦C to 22.5 ◦C, a time
series of the same length is drawn from values in this range in 0.25 K steps5.
This line defines a reasonable null-hypothesis for a correlation of presence by
chance.
Two different comparison methods have been used. Assuming that especially
the correct detection of presence is necessary, one can compare if the user pres-
ence was detected similarly. Figure 4.8 shows that 75 % of the temperature set
points are above 20 ◦C if the user is present for the Daum user. The same 75 %
can be achieved with a threshold of 16 ◦C for the Daum user. If the user is
absent, 75 % of the temperature set-points are below the respective threshold.
Therefore 20 ◦C for the Daum user and 16 ◦C for the PPD user were chosen as
threshold to determine the user presence. The bedroom is not included in this
50.25 K are the minimum step of the algorithm
135
4 Simulated Results
0 5 10 15 20 25 30 35 40
Days
0
20
40
60
80
100
Perc
enta
geo
fpre
sen
cem
atch
ing
Alternative Simulations
Random Time-Series
Figure 4.39: Presence matching between base simulation and the same simu-lations using a different initialization for the Daum user
analysis. The results are shown in figures 4.39 and 4.40.
Both figures show a number of gray lines, showing the percentage the pres-
ence of an alternative simulation matches the presence of the base simulation.
For the Daum user, the value starts at approximately 60 % at day one and than
quickly raises to values in the range of 80-95 % after the first user feedbacks were
incorporated. Over time the algorithm shows a slow decline of the matching but
stays always well above 80 %. For each of the eight alternative simulations the
different lines are indiscernible due to their similarity. The uncorrelated time-
line for comparison shows a matching of 60-70 % and is well discernible from
the other time-series. For the PPD user the results are similar, starting at 100 %,
the algorithms comparability drops to 60 % at day 3 and than raises to a value
above 80 % for the rest of the time. The presence matching is of different qual-
ity for weekdays and weekends. Weekends match approximately 10 % less well
than the weekdays. Presence on weekends shows a higher variety than the one
on weekdays, additionally the PPD user gives less feedback than the Daum user.
136
4.9 Independence from initialization
0 5 10 15 20 25 30 35 40
Days
0
20
40
60
80
100
Perc
enta
geo
fpre
sen
cem
atch
ing
Alternative Simulations
Random Time-Series
Figure 4.40: Presence matching between base simulation and the same simu-lations using a different initialization for the PPD user
This may result in the difference between weekdays and weekends that cannot
be noticed by the Daum user. The uncorrelated time-series matches in a range
of 70-85 %, with again a different behavior than the correlated time-series. This
shows in both cases, that the matching is not due to pure chance.
Similarity of time-series can also be tested by directly comparing the set-point
series with one another. This was done using Dynamic Time Warping (DTW),
see section B on page 185 for a short introduction and reasoning why this method
was chosen. DTW results in a distance between two time series, the smaller the
more similar those time series are.
The results for the different users are shown in figures 4.41 and 4.42. The DTW
values were calculated independently for each week, to show if any develop-
ment in the DTW-values takes place. With 7 days, 144 timesteps per day and
5 rooms under consideration (this time the bedroom was included), there are
5040 values per time-series. The gray lines indicate the DTW value for each
week between the base algorithm simulation and simulations using the same
137
4 Simulated Results
1 2 3 4 5 6
Weeks
0
2000
4000
6000
8000
10000
12000
14000
Cu
mm
ula
ted
dis
tan
ceo
fall
roo
ms
Alternative Simulations
Random Time-Series
Figure 4.41: Weekly DTW difference between the setpoint temperatures for theDaum user. All alternative simulations show significantly moreagreement with the original time series than the null hypothesis.
1 2 3 4 5 6
Weeks
0
2000
4000
6000
8000
10000
12000
14000
Cu
mm
ula
ted
dis
tan
ceo
fall
roo
ms
Alternative Simulations
Random Time-Series
Figure 4.42: Weekly DTW difference between the setpoint temperatures forthe PPD user. All alternative simulations show significantly moreagreement with the original time series than the null hypothesis.
138
4.9 Independence from initialization
0 1 2 3 4 5 6 7 8simulation run
7
6
5
4
3
2
1
0
1
Rel
ativ
e di
ffere
nce
in e
nerg
y de
man
d [%
]
Mean Energy Demand: 244 kWhStandard Deviation: 5.8 kWh
Figure 4.43: Comparison of the energy demand of the different simulations forthe Daum user
algorithm but different initalization values for the random number generator.
The red line again indicates the null-hypothesis of an uncorrelated, randomly
generated time series as described above. The correlated time-series show an
approximate value of 3000 and no change of the DTW-value over the course of
the simulation. All eight simulations are rather indistinguishable from one an-
other. In contrast, the null-hypothesis is clearly distinguishable from the other
simulations with its DTW of approximately 12 000 being fourfold higher than
the correlated time-series. Therefore it can be well assumed that any similarity
between these simulations is not by chance but due to a working algorithm.
Finally it is examined if energy demand and thermal comfort are similar. The
results are shown in figures 4.43-4.46. Shown are the differences between the
C*M-N version used in this work and the alternative results. The average energy
demand is 244±5.8 kWh for the Daum user, for the PPD user the demand is on
average 240± 7 kWh. This corresponds to a relative deviation of 3-4 %. With
an average duration of 303±1.2 h of the comfort value below its threshold for
139
4 Simulated Results
0 1 2 3 4 5 6 7 8simulation run
10
8
6
4
2
0
2
Rel
ativ
e di
ffere
nce
in e
nerg
y de
man
d [%
]
Mean Energy Demand: 240 kWhStandard Deviation: 7.0 kWh
Figure 4.44: Comparison of the energy demand of the different simulations forthe PPD user
0 1 2 3 4 5 6 7 8simulation run
0.6
0.4
0.2
0.0
0.2
0.4
0.6
0.8
Rel
ativ
e di
ffere
nce
in c
omfo
rt b
elow
thre
shol
d [%
]
Mean hours below threshold: 303 hStandard Deviation: 1.2 h
Figure 4.45: Comparison of the duration of low thermal comfort for the Daumuser with different initialization values
140
4.10 Independence from user models
0 1 2 3 4 5 6 7 8simulation run
0
1
2
3
4
5
6
7
8
Rel
ativ
e di
ffere
nce
in c
omfo
rt b
elow
thre
shol
d [%
]
Mean hours below threshold: 339 hStandard Deviation: 7.7 h
Figure 4.46: Comparison of the duration of low thermal comfort for the PPDuser with different initialization values
the Daum user and 339±7.7 h for the PPD user, the comfort values show only a
small variation of approximately 0.5 or 3 % respectively.
As the presence matching and the DTW analysis of the data already indicated,
the algorithm results in similar results independent of its initialization. Further-
more, comfort and energy demand is on a highly comparable level. Therefore,
it can be concluded that the algorithm creates comparable results independent
of the initialization of the random number generator.
4.10 Independence from user models
Throughout this work the two different user’s have shown the different effects
that a preference on higher temperature set-points has. But they have also
shown that despite different comfort rating methods the algorithm works suc-
cessfully. To further investigate the independence from the user’s feedback, this
section shows a similar analysis as conducted in section 4.2 on page 74. Again
141
4 Simulated Results
0.0 0.5 1.0 1.5 2.0 2.5 3.0
PMV
0
20
40
60
80
100
Pro
bab
ility
togi
vefe
edb
ack
in%
TH1.0
TH0.5
TH0.3
Figure 4.47: Relationship between feedback probability and PMV. The defaultuser TH0.5 does not give feedback if the PMV is below 0.5, theTH0.3 user may give feedback earlier while the TH1.0 user is lesslikely to give feedback
the static presence profile shown in figure 4.12 on page 88 is used to test if
the algorithm successfully adapts to the user’s presence profile. Section 4.2 de-
scribes how the threshold temperature Tset, th is derived to distinguish between
presence and absence. The temperature set-point distributions and the fitted
distributions are shown in the appendix in figure A.7. Table A.13 summarizes
the fitting parameters and the threshold temperature Tset, th.
Below a certain PMV threshold the PPD user does not give any feedback. This
threshold is 0.5 throughout this work. In this section two other thresholds are
used: 0.3 and 1.0. In the first case, the user will give feedback earlier, in the latter
case the user will give less feedback. If feedback is possible, the possibility will
be the same for each user. Figure 4.47 shows the probability of a “too cold” feed-
back depending on the PMV. The feedback probability is a non linear function
with respect to the PMV. For this section the Daum user is initialized with two
142
4.10 Independence from user models
0 5 10 15 20 25 30 35 40
Days
0.0
0.2
0.4
0.6
0.8
1.0
Perc
enta
geo
fmat
ched
stat
e
PPD TH0.5
PPD TH0.3
PPD TH1.0
Daum S1
Daum S5
Daum S6
Figure 4.48: The algorithms ability to match the user’s state for six different usertypes. The results show that the different user types do not signifi-cantly impact the algorithms ability to detect the user presence.
additional paramterizations. While the parameterization of subject 1 has been
used (refer to figure 3.16 on page 48 for details) throughout this work, in this
section the parameterization of subject 5 and subject 6 has been used addition-
ally. Subject 5 has a wider range of accepted temperatures and is less likely to
give a “too cold” feedback compared to subject 1. Subject 5 prefers lower tem-
peratures compared to subject 1 and subject 2. Also the probability of giving
“too cold” feedback is steeper.
Figure 4.48 shows a high rate of presence matching for each user after an initial
learning phase of the algorithm. All three Daum users show a similar behav-
ior: the state matching is very good for weekdays and has a very good but lower
matching rate at the weekends. Section 4.2 explains the reasons. The lesser
feedback of the PPD TH1.0 user leads to slightly worse state matching, espe-
cially for the presence part, because it is not unlikely that the user will not give
feedback, although the temperature is below Tset, th. A different effect shows
143
4 Simulated Results
the PPD TH0.3 user. The more often given feedback yields higher temperatures
resulting in a slightly worse matching of the absence state. Given that the dif-
ferences in state matching are rather minor, the algorithm has proven that it is
capable of detecting the user’s presence state independent of the implemented
feedback method.
144
5 Results from a �eld test
As already mentioned in section 3.3 a small field test has been conducted with
the adaptive algorithm. Data have been collected by sensor readings and an
additional questionnaire given to the users. The algorithm under test was an
older version than finally analyzed in this work. The main difference is that the
algorithm in the field test did not feature a pre-heating method of any sort.
5.1 Results from measurements
Prior to the installation of the adaptive algorithm, a temperature base line was
recorded for each user to allow for a comparison of measured temperatures.
Figures 5.1 and 5.2 show two different examples from two different apartments.
Each figure shows three lines. In gray the measured temperature before us-
ing the algorithm is shown with a 30 minutes resolution and averaged over one
week. One standard deviation is used to display the error-marks. The temper-
ature set-points calculated by the algorithm for the last day of the field test are
shown in bright red, the measured temperatures of this day are shown in a pale
red with a dashed line. The line of measured temperatures is rather steady be-
cause only temperature changes above 0.25 K are communicated by the sensor
in use.
For the study in one apartment(see figure 5.1) the originally measured temper-
ature is rather steady, on average between 18 ◦C and 19 ◦C. After using the al-
gorithm, the temperature set-point is increased at approximately 10:00 h until
145
5 Results from a field test
00:00 06:00 12:00 18:00 00:00
Hours of Day
14
15
16
17
18
19
20
21
Tem
pera
ture
in °
C
measured temperature with algorithmtemperature set-points with algorithmmeasured temperature without algorithm
Figure 5.1: Temperatures with and without the algorithm in a study. The algo-rithm’s set-point temperatures differ from the user’s previous tem-perature profile, with one clear peak in the afternoon and low tem-perature set-points for the rest of the day.
16:00 h, up to 21 ◦C. During the rest of the day the temperature set-point is nor-
mally well below the previous measured room temperatures. Due to the build-
ings thermal inertia the temperature cannot directly follow the set-point. With
a difference larger than 2 K between measured and set-point temperature in the
morning, it can be assumed that the TRV was closed which results in energy sav-
ings. This may have also been true while the algorithm was not working, given
that both measured temperatures are rather comparable. Given the use-case of
the study, the adaptation to the user seemed to work rather well. The user had
one child he took care of but from 09:00 h to 16:00 h it was in the kindergarten
and the user normally used this time to work. This behavior coincides well with
the temperature peak in the set-point profile between 10:00 h and 16:00 h.
The second apartment also shows a rather constant temperature over the whole
day, varying around 20 ◦C. The room temperature raises slightly around 12:00,
146
5.1 Results from measurements
00:00 06:00 12:00 18:00 00:00
Hours of Day
16
17
18
19
20
21
22
23
Tem
pera
ture
in °
C
measured temperature with algorithmtemperature set-points with algorithmmeasured temperature without algorithm
Figure 5.2: Temperatures with and without the algorithm in the living room ofa flat. The measured temperatures are lower in the morning and inthe evening compared to the previous profile.
probably due to solar radiation as the flat was normally not occupied during
the day. After implementing the algorithm the measured temperature is slightly
below the previous temperature, but shows a temperature set-point increase in
the morning at 06:30. As the living room was also used for breakfast, this is a
reasonable result. The effect of the missing pre-heating method in this version
of the algorithm can also be seen as the measured temperature reaches its max-
imum right at the end of the increased temperature phase at 08:00, probably too
late to cater to the user’s needs. Absent is a phase of increased temperature at
the evening, which is unexpected for a living room. But as the measured tem-
peratures are in the same range as measured temperatures before, the results
seem to fit the user’s needs.
From the data of the temperature profiles it is not possible to conclude if any
energy savings were possible due to the algorithm. The room temperatures
147
5 Results from a field test
0 1 2 3 4 5Completely False
False
neutral
True
Exactly True
Using the heating system was easy
Figure 5.3: Ease of use of the adaptive heating system. The majority of the usersrated the system as easy to use.
prior to the systems installation in figure 5.1 could well be the result of an un-
heated roo, which would lead to higher energy consumption with the adaptive
algorithm that shows a heating period at midday with temperature set-points
higher than measured temperatures, indicating an open TRV. Figure 5.2 shows
an on average reduced measured temperature compared to the baseline. Addi-
tionally, the set-point is often below the measured temperature indicating that
the TRV is partially or fully closed therefore this case used less energy with the
algorithm.
5.2 Results from user questionnaire
For the questionnaire it was important to find out if the users had problems
using the adaptive algorithm and if they were satisfied by the results the adap-
tive algorithm provided. The questionnaire consisted of different types of ques-
148
5.2 Results from user questionnaire
0 1 2 3 4Completely False
False
Neutral
True
Exactly True
The systems influence on the roomtemperature wascomprehensible
Figure 5.4: Comprehensibility of the adaptive heating system.
tions, most of them used pre-formulated answers. If users had to indicate if
they would agree with a statement, they could choose between five answers:
“Exactly True” indicating a high agreement with the statement, “True”, “neutral”
being ambiguous with the statement, “False” and “Completely False”, indicat-
ing no agreement with the given statement.
To access the ease of use of the adaptive system the users were asked if they
would agree with the statement that “The system is easy to use”. 7 out of 8
persons that responded to the questionnaire agreed with the statement, only
one person had trouble using the system. The results are shown in figure 5.3.
Only if the behavior of the adaptive algorithm is comprehensible to the user it
can be expected that the user is able to use the system successfully. Therefore
the users were asked to indicate their agreement to the statement “The sys-
tems influence on the room temperature was comprehensible”, the results to
this questions are shown in figure 5.4. 6 out of 8 stated that the system acted
comprehensible, one users opinion was neutral to the statement and one per-
149
5 Results from a field test
son did not agree to the statement.
To address the question if the system was able to adapt to the user’s schedule,
the user was asked how long it took for the system to adapt. They were able to
choose between the five answers “After one day”, “After a few days”, “After one
week”, “‘More than a week” and “System did not adapt”. The first three answers
are rated as a success for the system. Prior to the field test the users were clus-
tered on their self assessment if they agreed to the statement “I have a regular
schedule that is the same every week”. For every user with a regular schedule
the system could adapt successfully within one week. Users who claimed to
have an irregular schedule were less satisfied. Two of them found the system to
adapt to their needs although it took more than one week, two others had the
impression that the system did not adapt at all.
The users were also asked how often they felt “too cold” or “too warm”, not
only for the adaptive system but also for the previously installed TRVs. The
results are shown in figure 5.5. Possible answers to the statement “I was feeling
cold/warm...” were “always”, “daily”, “several times per week”, “once a week”,
“less than once a week”, “never”. The results were rather surprising. 6 out of
8 users felt once a week or more often too cold with their previously installed
TRVs, while 5 out of 8 never felt too cold with the adaptive system. On the other
hand, 3 out of 8 users felt too warm at least several times per week with the
adaptive algorithm. And although the system is designed to continuously re-
duce the temperature set-point, users felt more often too warm with the algo-
rithm than with their old TRVs. This finding is interesting and indicates, al-
though usage was described as easy, some problems with using the system. By
giving feedback the user can actively decrease the temperature set-point, in ad-
dition to the systematic decreases. With a lower set-point limit of 10 ◦C users
should be able to adapt to their comfort temperature.
The simulations could not answer the question if the adaptive system was easy
to use for a real-world user. Given the simplicity of the system it was assumed
150
5.2 Results from user questionnaire
0 1 2 3 4 5
Never
less than once aweek
once a week
several times perweek
daily
always
I was feeling too cool ...
normal TRVs
adaptive algorithm
0 1 2 3 4 5
Never
less than once aweek
once a week
several times perweek
daily
always
I was feeling too warm...
normal TRVs
adaptive algorithm
Figure 5.5: Times of feeling too cold or too warm for both heating systems. Us-ing the adaptive algorithm, users felt less often too cold. And al-though the algorithm was built to reduce the room temperature,some users felt too warm with the algorithm.
and the field test provided a hint that this assumption is reasonable. Due to
the small number of participants the results are only indications rather than
proof. The adaptive algorithm of the field test and the one used in simulations
was mainly the same. For users with a regular schedule the results indicate
that the favorable results of the simulations can also be achieved in a real world
scenario, although people with an irregular schedule may face problems. De-
151
5 Results from a field test
spite the fact that the pre-heating algorithm was missing, only six out of eight
people claimed that they were to cold more often than once a week, while four
users claimed that they were to warm. While the heating system in the sim-
ulation was sized to standards and did not posses much additional power for
fast reheating, the good thermal ratings despite a missing pre-heating method
indicate that the real world heating systems possess enough spare power to re-
heat a room faster. This is especially interesting as the simulations have shown
that a pre-heating method is crucial for the adaptive system to work properly.
Although the adaptive heating system implemented a system to decrease room
temperatures, a room being too cold was more often a problem if a normal TRV
was used than the adaptive algorithm. This may be an indication that people
do have problems to use a TRV correctly to ensure their thermal comfort. This
may result in less possible energy savings than suggested by the simulations.
152
6 Conclusion
This work has shown an algorithm that uses a simple user feedback to estimate
a user’s presence and his preferred temperature on the scale of single rooms
in a building. Goal of the algorithm was to maintain the user’s thermal com-
fort while simultaneously saving energy. As literature research has shown, most
rooms are constantly heated. Intermittent heating, meaning that the tempera-
ture is lowered on absence and the rooms are reheated prior to the user’s return,
should result in energy savings without impacting the user’s comfort. The algo-
rithm was tested in simulations as well as a field test. Besides learning about the
user’s behavior, the algorithm can also learn about the building’s thermal prop-
erties to adapt pre-heating times and overall ensure the user’s thermal comfort.
The algorithm was tested against two reference cases. In the first reference
case the rooms were constantly heated to 21 ◦C at day and 18 ◦C at night. In
the second reference case the user raised the temperature to 21 ◦C on entrance
and lowered it to 16 ◦C when leaving. Compared to a constantly heated room,
energy savings in the range of 10 %-20 % have been shown with a compara-
ble comfort level. These energy savings are independent of a buildings insu-
lation standard. In contrast to a constantly heated room, an unexpected return
usually results in a decreased thermal comfort. Also, slower than expected re-
heating of the room impacted the user’s comfort. Compared to an intermittent
heating user, no energy savings are possible. Usually, more energy is needed.
The algorithm uses more energy than the second reference case, because first,
it cannot know for sure if the user is at home. Therefore a room is heated longer
than if it is only heated on demand by the user. Second, the algorithm is focused
on providing thermal comfort and thus implements a pre-heating method that
153
6 Conclusion
results in additional heating time. However, thermal comfort was very low for
the intermittent heating user. Using the algorithm, thermal comfort is signifi-
cantly improved, mainly due to the room being pre-heated. The best method
found to ensure a sufficiently pre-heated room was a slope based pre-heating
method that adapts a room’s minimum temperature level and the inclination
of the slope based on the previous days.
Additionally, two different behavioral models have been used. The first behav-
ioral model used three probability distributions if the air temperature was too
cold, too warm or good. Only air temperature was used as input variable. The
second user judged on global temperature and additional variables like cloth-
ing and metabolic rate or air humidity which were not simulated and therefore
replaced by fixed values. Both users had different thermal preferences, the first
user preferred temperatures around 23 ◦C while the second one preferred tem-
peratures in a range around 21 ◦C. These different preferences were reflected in
the resulting temperature profiles. A higher preferred temperature was accom-
panied by considerably longer pre-heating times and higher minimum temper-
atures. Especially heating systems that were powerful enough to ensure a con-
stant heating but did not have enough power for intermittent heating needed
long pre-heating times to ensure thermal comfort.
In general, a more powerful heating system seems to help the adaptive algo-
rithm. With a more powerful system, pre-heating times can be shorter and the
thermal comfort is maintained better. This may result in the positive outcome
of a lower energy demand combined with higher user satisfaction. The max-
imal power of a water based heating system can be increased by raising the
heating curve. This can normally be done at the boiler’s control table without
any need for changes in the hydraulic system. In addition to the water based
radiator heating system with an increased flow temperature, a faster reacting
system with a higher radiative ratio has been tested. Such a system represents,
for example, an electric radiant heating system. Because low surface tempera-
tures are a concern if one uses intermittent heating, a heating system that heats
154
6 Conclusion
the surface directly, was expected to be advantageous for the algorithm. This
assumption was confirmed.
Most of the results have been computed with the help of simulations. But also
a field test confirmed the functionality of the algorithm. It also confirmed that
the implementation with a simple push button as user interface was easy to
use. For some users it even seems to be easier to use than the standard ra-
diator valve, because they were more satisfied with results from the adaptive
algorithm than results from their TRV. The field test indicated a rebound ef-
fect, because some users used higher temperature set-points with the adaptive
algorithm than without. Therefore the energy savings could be smaller than
expected from the simulated results.
Besides thermal comfort and energy efficiency, ease of use was another issue to
be addressed. The field test has indicated that the user were capable of using
the system. For some users the results were better than with their previously
used TRVs. Additionally, they mostly found the system comprehensible. With
respect to figure 2.3 this system was considered as easy to learn and easy to re-
member. The question if the system was efficient to use cannot be answered
from the questionnaire, although no user stated that using the system was te-
dious. The simulation shows that the first user, who preferred slightly higher
temperature set-points, had to give 450-500 feedbacks over 42 days, which is
more than 10 per day. This is less interaction than necessary for the second ref-
erence case but more than in the first reference case and overall rated as accept-
able. The second user instead had to give 40-50 feedbacks that is only 1-2 per
day1. This is rated as a very good result. Therefore the system can be assumed
to be also efficient to use. If the system shows few errors and is subjectively
pleasing was not accessed by the questionnaire.
1The feedback mechanism and threshold was different for these users and results are therefore notdirectly comparable. Additionally, the algorithm was set to use 21 ◦C as minimum temperatureif the user gives a “too cold” feedback. This value coincides with the second user’s preferredtemperature, but is 2 K below the first user’s preferred temperature.
155
6 Conclusion
Overall this work shows by field test and simulations that the developed al-
gorithm is capable of identifying the characteristic user behavior using a very
simple user interface. Detecting the characteristic user behavior enables en-
ergy savings due to intermittent heating. The algorithm was able to completely
automate the intermittent heating which is an important part for user accep-
tance. Furthermore these energy savings were possible without reducing the
user’s thermal comfort.
156
7 Outlook
The results for the adaptive algorithm are promising with regards to thermal
comfort and energy efficiency, especially if an adaptive pre-heating approach is
used. Nonetheless, learning algorithms can always be improved to yield better
results. This section will give an outlook in which way the results of the algo-
rithm could be improved further and how the algorithm could become part of
a larger system to further improve energy efficiency.
One measure to improve the results of the algorithm would be tweaking how
the adaptation works. That the aggressiveness of the algorithm on adapting the
profile is reduced over time is a crucial part of the algorithm. But if the user
behavior changes significantly, the algorithm would have trouble to adapt to
a new schedule. By monitoring the average number of feedback this situation
can be detected and the aggressiveness of the algorithm could be determined
dynamically. As a result, changes in user behavior could be incorporated faster
into the temperature profiles.
Also a vacation detection could be implemented. If no feedback in any room is
given for a pre-defined threshold, the algorithm switches to a vacation mode,
reducing the temperature to 10 ◦C and restores the temperature profiles on the
first given feedback in any room. To avoid cooling of the building if a vacation
was assumed wrongly, the temperature set-point could be reduced slowly down
to 10 ◦C and not directly in one step.
The user feedbacks could also be used to gather more information about the
user’s preferences: Given the user feedbacks, his preferred temperature could
be calculated from this data. With this information the algorithm could be fur-
157
7 Outlook
ther improved. In the current implementation of the algorithm a “too cold”
feedback will increase the temperature set-point to at least 21 ◦C, independent
of the previous set-point to avoid a situation where the set-point is increased
from 12 ◦C to 13 ◦C. This fixed minimum temperature on “too cold” feedback
could be replaced by the user specific value, dynamically calculated by his feed-
backs.
The clustered learning was described as the theoretically most reasonable of
the three learning methods. In this work the cluster were fixed, as the user’s
behavior was known to be similar on weekdays and different on the weekend.
But of course temperature set-points and feedbacks could be used to cluster
these days and determine the cluster dynamically. The Dynamic Time Warp-
ing method explained in section B would be an option to detect similarities, for
example. Learning could be further improved with a method to detect unex-
pected behavior. One time temperature increases because of an out of the or-
dinary day, are normally reduced in due time by the systematic decreases of the
algorithm. Nevertheless, analyzing previous days could mark some feedbacks
as unexpected, resulting in an increased chance for a systematic decrease on
the next day, to further improve energy savings and to reduce the effect of one-
time events.
Besides those general improvements of the algorithm, the adaptive algorithm
could be deployed in larger system to further improve energy efficiency. It has
been shown that a more powerful heating system may result in higher user
comfort and energy savings. So the algorithm could analyze if the flow tem-
perature in a water based heating system should be increased. At least heating
systems with boiler are not too sensitive to flow temperature. A different case
would be a system, where a heat pump is used as heat source. The lower the
flow temperature is, the more efficient is a heat pump. So it would be reason-
able to use the gathered data to calculate the lowest possible flow temperature
depending on the current conditions, like ambient temperature, that allows
sufficient heating of the room. Furthermore the temperature profiles could be
158
7 Outlook
translated into a demand forecast and be used to improve the heat pumps effi-
ciency. If, for example, no high demand is expected for the next hours, replen-
ishing the storage can be postponed. This makes sense if a weather forecast
supposes that the upcoming weather conditions would be more beneficial for
the heat pump’s efficiency. Also in connection with smart grids and power to
heat solutions, demand forecasts based on the temperature profiles could be
used to shift loads.
159
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Version: 2013
Appendix
A Further Results
Most of the time only results for the medium energy demand case have been
shown. This section shows additional data for the low and high energy cases.
A.1 Result tables for low and high energy demand
scenarios
This section summarizes the result tables for the different energy demand sce-
narios not shown in the text.
171
A Further Results
Table A.1: Results for the clustered learning algorithm for both behavioral mod-els without pre-heating and low energy demand
CDL-N CPL-N
Qsim [kWh] 180.88 162.96
Qsim relative to RSL [%] 65 58
PPD criteria not met [h] - 288.50
Thereof too cold [h] - 288.50
Daum criteria not met [h] 289.00 -
Thereof too cold [h] 289.00 -
PPD criteria relative to REL [%] - 125
Daum criteria relative to REL [%] 98 -
Total number of feedbacks 503 217
Table A.2: Results for the clustered learning algorithm for both behavioral mod-els without pre-heating and high energy demand
CDH-N CPH-N
Qsim [kWh] 268.55 262.52
Qsim relative to RSH [%] 60 59
PPD criteria not met [h] - 340.00
Thereof too cold [h] - 340.00
Daum criteria not met [h] 296.00 -
Thereof too cold [h] 296.00 -
PPD criteria relative to REH [%] - 131
Daum criteria relative to REH [%] 100 -
Total number of feedbacks 453 258
172
A.1 Result tables for low and high energy demand scenarios
Table A.3: Results for the clustered learning algorithm for both behavioral mod-els with slope based pre-heating and low energy demand
CDL-S5 CPL-S5
Qsim [kWh] 250.76 228.45
Qsim relative to RSL [%] 89 81
PPD criteria not met [h] - 54.67
Thereof too cold [h] - 54.67
Daum criteria not met [h] 76.17 -
Thereof too cold [h] 42.50 -
PPD criteria relative to REL [%] - 24
Daum criteria relative to REL [%] 26 -
Total number of feedbacks 540 46
Table A.4: Results for the clustered learning algorithm for both behavioral mod-els with slope based pre-heating and high energy demand
CDH-S5 CPH-S5
Qsim [kWh] 373.91 354.39
Qsim relative to RSH [%] 84 80
PPD criteria not met [h] - 107.33
Thereof too cold [h] - 107.33
Daum criteria not met [h] 136.17 -
Thereof too cold [h] 136.17 -
PPD criteria relative to REH [%] - 41
Daum criteria relative to REH [%] 46 -
Total number of feedbacks 519 79
173
A Further Results
Table A.5: Results for the clustered learning algorithm for the Daum user withblock based pre-heating and low energy demand
CDL-B1 CDL-B2 CDL-B3 CDL-B4
Qsim [kWh] 181.06 215.57 215.54 267.25
Qsim relative to RSL [%] 65 77 77 95
Qsim relative to CDL-N [%] 100 119 119 148
Daum criteria not met [h] 261.00 206.00 181.50 174.67
Thereof too cold [h] 261.00 206.00 138.83 92.50
Daum criteria relative toREL [%]
88 70 61 59
Daum criteria relative toCDL-N [%]
90 71 63 60
Total number of feedbacks 381 411 496 481
Table A.6: Results for the clustered learning algorithm for the PPD user withblock based pre-heating and low energy demand
CPL-B1 CPL-B2 CPL-B3 CPL-B4
Qsim [kWh] 175.25 189.33 210.95 235.44
Qsim relative to RSL [%] 63 68 75 84
Qsim relative to CPL-N [%] 108 116 129 144
PPD criteria not met [h] 240.50 211.67 156.33 68.67
Thereof too cold [h] 240.50 211.67 156.33 68.67
PPD criteria relative toCPL-N [%]
83 73 54 24
PPD criteria relative to REL[%]
104 92 68 30
Total number of feedbacks 122 101 70 46
174
A.1 Result tables for low and high energy demand scenarios
Table A.7: Results for the clustered learning algorithm for the Daum user withblock based pre-heating and high energy demand
CDH-B1 CDH-B2 CDH-B3 CDH-B4
Qsim [kWh] 283.51 307.33 297.97 397.58
Qsim relative to CDH-N [%] 106 114 111 148
Qsim relative to RSH [%] 64 69 67 89
Daum criteria not met [h] 283.33 265.00 225.67 209.50
Thereof too cold [h] 283.33 265.00 225.67 209.50
Daum criteria relative toREH [%]
96 89 76 71
Daum criteria relative toCDH-N [%]
96 90 76 71
Total number of feedbacks 295 390 418 491
Table A.8: Results for the clustered learning algorithm for the PPD user withblock based pre-heating and high energy demand
CPH-B1 CPH-B2 CPH-B3 CPH-B4
Qsim [kWh] 281.64 294.59 310.89 396.27
Qsim relative to RSH [%] 63 66 70 89
Qsim relative to CPH-N [%] 107 112 118 151
PPD criteria not met [h] 288.50 264.83 232.17 169.17
Thereof too cold [h] 288.50 264.83 232.17 169.17
PPD criteria relative to REH[%]
111 102 89 65
PPD criteria relative toCPH-N [%]
85 78 68 50
Total number of feedbacks 132 116 101 80
175
A Further Results
Table A.9: Results for the different learning methods for the Daum user withadaptive slope-based pre-heating and low energy demand
CDL-SA DDL-SA WDL-SA
Qsim [kWh] 250.74 262.44 233.62
Qsim relative to RSL [%] 89 94 83
Daum criteria not met [h] 79.00 191.17 57.50
Thereof too cold [h] 28.17 24.33 50.17
Daum criteria relative to REL [%] 27 65 19
Total number of feedbacks 462 532 538
Table A.10: Results for the different learning methods for the PPD user withadaptive slope-based pre-heating and low energy demand
CPL-SA DPL-SA WPL-SA
Qsim [kWh] 235.88 236.04 218.39
Qsim relative to RSL [%] 84 84 78
PPD criteria not met [h] 38.33 41.67 76.33
Thereof too cold [h] 38.33 41.67 76.33
PPD criteria relative to REL [%] 17 18 33
Total number of feedbacks 38 34 58
176
A.1 Result tables for low and high energy demand scenarios
Table A.11: Results for the different learning methods for the Daum user withadaptive slope-based pre-heating and high energy demand
CDH-SA DDH-SA WDH-SA
Qsim [kWh] 390.17 420.47 372.96
Qsim relative to RSH [%] 88 95 84
Daum criteria not met [h] 110.50 147.83 98.67
Thereof too cold [h] 72.00 63.50 98.67
Daum criteria relative to REH [%] 37 50 33
Total number of feedbacks 495 485 509
Table A.12: Results for the different learning methods for the PPD user withadaptive slope-based pre-heating and high energy demand
CPH-SA DPH-SA WPH-SA
Qsim [kWh] 363.95 395.96 362.70
Qsim relative to RSH [%] 82 89 82
PPD criteria not met [h] 48.33 46.67 76.33
Thereof too cold [h] 48.33 46.67 76.33
PPD criteria relative to REH [%] 19 18 29
Total number of feedbacks 44 34 65
177
A Further Results
A.2 Results overview for high and low energy demand
scenarios
This sections shows the overview about the different algorithms with and with-
out pre-heating (adaptive and static) and compares them to the to reference
cases for both behavioral models and low and high energy demand. For a de-
tailed description of these figures please refer to the end of section 4.4 on page 112
0.0 0.2 0.4 0.6 0.8 1.0Relative energy consumption [lower is better]
0.0
0.2
0.4
0.6
0.8
1.0
Rel
ativ
e am
ount
of d
isco
mfo
rt [l
ower
is b
ette
r]
CDL-N
CDL-S5
CDL-B1
CDL-B2CDL-B3 CDL-B4
CDL-SA
CDL-BA
RSL
REL
Figure A.1: Comparison of the results of the different algorithm versions with
respect to energy, comfort and number of feedbacks for the Daum
user and low energy demand
178
A.2 Results overview for high and low energy demand scenarios
0.0 0.2 0.4 0.6 0.8 1.0Relative energy consumption [lower is better]
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Rel
ativ
e am
ount
of d
isco
mfo
rt [l
ower
is b
ette
r]
CPL-N
CPL-S5
CPL-B1CPL-B2
CPL-B3
CPL-B4CPL-SA
CPL-BA
RSL
REL
Figure A.2: Comparison of the results of the different algorithm versions withrespect to energy, comfort and number of feedbacks for the PPDuser and low energy demand
0.0 0.2 0.4 0.6 0.8 1.0Relative energy consumption [lower is better]
0.0
0.2
0.4
0.6
0.8
1.0
Rel
ativ
e am
ount
of d
isco
mfo
rt [l
ower
is b
ette
r]
CDH-N
CDH-S5
CDH-B1CDH-B2
CDH-B3CDH-B4
CDH-SA
CDH-BA
RSH
REH
Figure A.3: Comparison of the results of the different algorithm versions withrespect to energy, comfort and number of feedbacks for the Daumuser and high energy demand
179
A Further Results
0.0 0.2 0.4 0.6 0.8 1.0Relative energy consumption [lower is better]
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Rel
ativ
e am
ount
of d
isco
mfo
rt [l
ower
is b
ette
r]
CPH-N
CPH-S5
CPH-B1CPH-B2
CPH-B3
CPH-B4
CPH-SA
CPH-BA
RSH
REH
Figure A.4: Comparison of the results of the different algorithm versions withrespect to energy, comfort and number of feedbacks for the PPDuser and high energy demand
180
A.3 Devlopment of the user Feedback
A.3 Devlopment of the user Feedback
Day 1 Day 21 Day 41
CDL-N
01020304050607080
Day 1 Day 21 Day 41
CPL-N
Nu
mb
ero
ffee
db
acks
Number of too cold feedbacks per Day
too cold ignored too cold
Figure A.5: Feedbacks given over the time of the simulation for the clustered
learning method without pre-heating for both behavioral models
and low energy demand
181
A Further Results
Day 1 Day 21 Day 41
CDH-N
0102030405060708090
Day 1 Day 21 Day 41
CPH-N
Nu
mb
ero
ffee
db
acks
Number of too cold feedbacks per Day
too cold ignored too cold
Figure A.6: Feedbacks given over the time of the simulation for the clustered
learning method without pre-heating for both behavioral models
and high energy demand
A.4 Temperature distributions and curve-�tting results
This section summarizes the results of the curve fitting to the temperature set-
point distributions and the chosen threshold temperature for different user types.
Table A.13 shows the fitting parameters, figure A.7 the temperature set-point
distributions for the six different user types under consideration.
182
A.4 Temperature distributions and curve-fitting results
0.00
0.05
0.10
0.15
0.20
0.25
Rel
ativ
eFr
equ
ency
PPD TH0.5 PPD TH0.3
0.00
0.05
0.10
0.15
0.20
0.25
Rel
ativ
eFr
equ
ency
PPD TH1.0 Daum S1
8 10 12 14 16 18 20 22 24
Temperature in ◦C
0.00
0.05
0.10
0.15
0.20
0.25
Rel
ativ
eFr
equ
ency
Daum S5
8 10 12 14 16 18 20 22 24 26
Temperature in ◦C
Daum S6
Figure A.7: Temperature set-point distributions for six different user types andthe fitted skewed normal distribution
183
A Further Results
Table A.13: Fitting parameters for skewed normal distribution with µ the distri-butions mean, σ its standard deviation and v its skewness.
Simulation µ σ2 v Tset, th
Daum S1 14.90 10.00 3.77 19.0
Daum S5 14.83 9.94 3.90 19.0
Daum S6 11.80 9.78 3.79 18.5
PPD TH0.3 11.37 9.71 3.82 19.0
PPD TH0.5 10.60 9.74 3.80 19.0
PPD TH1.0 10.89 9.67 3.80 19.0
184
B Dynamic Time Warping
Comparing time series is an often needed method but far from being trivial or
straightforward. The most simple approach would be comparing the values at
each time step and is given by the function:
dLn (x , y) =(
M∑i=1
(xi − yi )n
) 1n
(B.1)
with n ∈ N, M the length of the time series x and y and xi and yi the i-th ele-
ment of its respective time series. For n = 2 it corresponds to the the Euclidean
distance. This method works only for time-series of the same length and the
quality of this comparison is questionable because a simple phase shift of a
time-series may result in a bad match of otherwise good matching series. One
example would be the sinus and cosine function. They match exactly in ampli-
tude and periodicity but due to their phase shift of π2 the Euclidean distance is
rather large. Its rather wide application is justified by its simplicity and compu-
tational speed especially in combination with Fourier transformations.
These drawbacks of this simple approach can be overcome by a method called
“Dynamic Time Warping” which matches the time steps by minimizing the dis-
tance between each time step through the function:
Di , j = f (xi , y j )+min(Di , j−1,Di−1, j ,Di−1, j−1), (B.2)
which recursively matches a path between the nearest neighbors. Batra [2016]
gives a great example how the implementation of this algorithm works and
185
how the warp-path is chosen. DTW is according to Serrà and Arcos [2014] the
benchmark for methods to determine the similarity of a timeline. And although
Serrà and Arcos [2014] find that a newer method, the time-warped edit distance
(TWED) outperforms DTW, DTW is used as a measure in this work because it
is scientifically established and accepted. The DTW python implementation
described in Albanese et al. [2012] is used.
186
Danksagung
Ein kluger Mensch hat einmal zu mir gesagt, die Danksagung ist eh das, was am
meisten gelesen wird. Grund genug, sich an dieser Stelle ein wenig Mühe zu
geben.
Wie üblich gilt der erste Dank natürlich meinem Doktorvater Univ.-Prof. Dr.-
Ing. Dirk Müller. Er hat einem immer die nötige Freiheit gelassen, auch eigene
Ansätze zu verfolgen, stand aber immer bereit, Fragen und Anregungen zu geben.
Nach einem Gespräch kam man immer klüger aus dem Büro - und mit mehr
Arbeit. Um die Promotion zu Ende zu bringen, musste man diese zusätzliche
Arbeit irgendwann ignorieren. Das heißt aber auch, dass sämtliche Mängel
auf meine Kappe gehen. Zweiter Berichter war Univ.-Prof. Dr. rer. nat. Mar-
tin Frank, der bislang eigentlich immer nur Umzugskartons für Nina mit mir
zusammen geschleppt hat, aber sich unproblematisch bereit erklärt hat zu helfen,
als im Kleingedruckten doch nach der Bedarf nach einem Naturwissenschaftler
auftauchte (und ja, Mathematik ist im strengen Sinn keine Naturwissenschaft).
Danke für das unproblematische Einspringen.
Beim direkten entstehen der Arbeit hat vor allem Peter Remmen durch das Gegen-
lesen einen wichtigen Beitrag geleistet. Alle jene, die die Arbeit unstrukturiert
und unverständlich fanden: Das war vorher noch viel schlimmer. Die beiden
Masterarbeiten von Bruno Lupulescu und Michael Behnke waren als Input für
diese Arbeit essenziell. Zusätzlich war es eine Freude mit Euch zusammen zu
arbeiten.
Gelingen kann so ein lange Arbeit aber eigentlich nur, wenn das Umfeld stimmt,
und an fantastischen Kollegen war das EBC sicherlich nicht arm. Sei es Nina
187
Kopmann, die mit mir das Büro geteilt hat, selten einer Meinung war bei wirk-
lich unwichtigen Themen und mit der ich diese Details ordentlich diskutieren
konnte. Man kann sich vielleicht eine bessere Bürokollegin wünschen - eine
Bessere zu bekommen wird schwer. Rita Streblow war als Oberingenieurin im-
mer hilfreich zur Stelle und musste gelegentlich geduldig zuhören, wenn mein
Blutdruck dringend wieder in gesündere Regionen gebracht werden musste.
Ihre Gelassenheit und innere Ruhe wird mir immer ein unerreichbares Ziel bleiben.
Damit mein Herz dem hohen Blutdruck überhaupt gewachsen ist, hat mich Kai
Rewitz häufig genug beim joggen durch die Melaten gescheucht, während sein
Bürokollege Mark Wesseling mit der Eiskasse versucht hat, sämtlichen sportlichen
Ausflüge wieder zu nichte zu machen. Im Winter konnte man als weniger kalte
Alternative immer auf die Süßigkeiten von Tanja Osterhage zurückgreifen. Während
Tanjas Vorräte meine Figur aus der Form gebracht haben, hat Tanja Vorträge,
Poster und Grafiken mit ihrem unfassbaren Blick für Details immer sicher in
Form gebracht. Als eine meiner Teamleiterinnen gebührt ihr ebenso wie Kris-
tian Huchtemann mein Dank für die Begleitung meiner beruflichen Laufbahn.
Die Mittagspausen wurden häufig durch spannende (andere würden sagen: anstren-
gende) Diskussionen bereichert, hierzu haben insbesondere Johannes Fütterer
und Paul Mathis beigetragen. Wenn eher Zeit zum Phrasendreschen war, waren
(neben anderen) Sebastian Stinner, Martin Schmidt und Henryk Wolisz immer
gute Sparrings-Partner.
Diese Liste kann - auch aus Platzgründen - nur unvollständig sein, daher auch
allen anderen Kollegen meinen herzlichen Dank. Ihr habt auf diversen Feiern,
Internen Seminaren, Ausflügen und natürlich auch bei der Arbeit mit Eurem
Humor, Eurer Hilfsbereitschaft und Eurem Input dafür gesorgt, dass das EBC
ein Ort war, an dem ich immer gern gearbeitet habe.
Eine Dissertation entsteht nie im luftleeren Raum, sie muss auch immer vom
eigenen Umfeld getragen werden. Daher möchte ich mich bei meinen Eltern
Gitta und Hermann Adolph bedanken, die den Grundstein für meine private
und berufliche Entwicklung gelegt haben. Wer immer mal wieder an meiner
188
guten Kinderstube gezweifelt hat: Das ist nicht die Schuld meiner Eltern, das
war eine persönliche Entscheidung von mir. Ebenfalls bin ich meiner Schwester
Christine Rudolph dankbar, die meine Arbeit mit Blick auf das Sprachliche gegen-
gelesen hat. Meine Schwiegereltern Beate und Franz-Josef Wirtz haben immer
wieder auf meine Frau (bedingt notwendig) und Kinder (ziemlich notwendig)
aufgepasst, wenn ich etwas mehr Zeit brauchte, um die Arbeit fertig zu stellen,
dafür herzlichen Dank. Der größte Dank gilt natürlich meiner Frau und meinen
Kindern, die sicherlich unter der zusätzlichen Arbeit und meiner häufigeren
Abwesenheit zurück gesteckt und mich dennoch unterstütz haben. Versprochen,
ich schreibe nicht noch eine Diss.
Michael Adolph, im Januar 2018.
E.ON ERC Band 1
Streblow, R.
Thermal Sensation and
Comfort Model for
Inhomogeneous Indoor
Environments
1. Auflage 2011
ISBN 978-3-942789-00-4
E.ON ERC Band 2
Naderi, A.
Multi-phase, multi-species
reactive transport modeling as
a tool for system analysis in
geological carbon dioxide
storage
1. Auflage 2011
ISBN 978-3-942789-01-1
E.ON ERC Band 3
Westner, G.
Four Essays related to Energy
Economic Aspects of
Combined Heat and Power
Generation
1. Auflage 2012
ISBN 978-3-942789-02-8
E.ON ERC Band 4
Lohwasser, R.
Impact of Carbon Capture and
Storage (CCS) on the European
Electricity Market
1. Auflage 2012
ISBN 978-3-942789-03-5
E.ON ERC Band 5
Dick, C.
Multi-Resonant Converters as
Photovoltaic Module-
Integrated Maximum Power
Point Tracker
1. Auflage 2012
ISBN 978-3-942789-04-2
E.ON ERC Band 6
Lenke, R.
A Contribution to the Design of
Isolated DC-DC Converters for
Utility Applications
1. Auflage 2012
ISBN 978-3-942789-05-9
E.ON ERC Band 7
Brännström, F.
Einsatz hybrider RANS-LES-
Turbulenzmodelle in der
Fahrzeugklimatisierung
1. Auflage 2012
ISBN 978-3-942789-06-6
E.ON ERC Band 8
Bragard, M.
The Integrated Emitter Turn-
Off Thyristor - An Innovative
MOS-Gated High-Power
Device
1. Auflage 2012
ISBN 978-3-942789-07-3
E.ON ERC Band 9
Hoh, A.
Exergiebasierte Bewertung
gebäudetechnischer Anlagen
1. Auflage 2013
ISBN 978-3-942789-08-0
E.ON ERC Band 10
Köllensperger, P.
The Internally Commutated
Thyristor - Concept, Design
and Application
1. Auflage 2013
ISBN 978-3-942789-09-7
E.ON ERC Band 11
Achtnicht, M.
Essays on Consumer Choices
Relevant to Climate Change:
Stated Preference Evidence
from Germany
1. Auflage 2013
ISBN 978-3-942789-10-3
E.ON ERC Band 12
Panašková, J.
Olfaktorische Bewertung von
Emissionen aus Bauprodukten
1. Auflage 2013
ISBN 978-3-942789-11-0
E.ON ERC Band 13
Vogt, C.
Optimization of Geothermal
Energy Reservoir Modeling
using Advanced Numerical
Tools for Stochastic Parameter
Estimation and Quantifying
Uncertainties
1. Auflage 2013
ISBN 978-3-942789-12-7
E.ON ERC Band 14
Benigni, A.
Latency exploitation for
parallelization of
power systems simulation
1. Auflage 2013
ISBN 978-3-942789-13-4
E.ON ERC Band 15
Butschen, T.
Dual-ICT – A Clever Way to
Unite Conduction and
Switching Optimized
Properties in a Single Wafer
1. Auflage 2013
ISBN 978-3-942789-14-1
E.ON ERC Band 16
Li, W.
Fault Detection and
Protection inMedium
Voltage DC Shipboard
Power Systems
1. Auflage 2013
ISBN 978-3-942789-15-8
E.ON ERC Band 17
Shen, J.
Modeling Methodologies for
Analysis and Synthesis of
Controls and Modulation
Schemes for High-Power
Converters with Low Pulse
Ratios
1. Auflage 2014
ISBN 978-3-942789-16-5
E.ON ERC Band 18
Flieger, B.
Innenraummodellierung einer
Fahrzeugkabine
in der Programmiersprache
Modelica
1. Auflage 2014
ISBN 978-3-942789-17-2
E.ON ERC Band 19
Liu, J.
Measurement System and
Technique for Future Active
Distribution Grids
1. Auflage 2014
ISBN 978-3-942789-18-9
E.ON ERC Band 20
Kandzia, C.
Experimentelle Untersuchung
der Strömungsstrukturen in
einer Mischlüftung
1. Auflage 2014
ISBN 978-3-942789-19-6
E.ON ERC Band 21
Thomas, S.
A Medium-Voltage Multi-
Level DC/DC Converter with
High Voltage Transformation
Ratio
1. Auflage 2014
ISBN 978-3-942789-20-2
E.ON ERC Band 22
Tang, J.
Probabilistic Analysis and
Stability Assessment for Power
Systems with Integration of
Wind Generation and
Synchrophasor Measurement
1. Auflage 2014
ISBN 978-3-942789-21-9
E.ON ERC Band 23
Sorda, G.
The Diffusion of Selected
Renewable Energy
Technologies: Modeling,
Economic Impacts, and Policy
Implications
1. Auflage 2014
ISBN 978-3-942789-22-6
E.ON ERC Band 24
Rosen, C.
Design considerations and
functional analysis of local
reserve energy markets for
distributed generation
1. Auflage 2014
ISBN 978-3-942789-23-3
E.ON ERC Band 25
Ni, F.
Applications of Arbitrary
Polynomial Chaos in Electrical
Systems
1. Auflage 2015
ISBN 978-3-942789-24-0
E.ON ERC Band 26
Michelsen, C. C.
The Energiewende in the
German Residential Sector:
Empirical Essays on
Homeowners’ Choices of
Space Heating Technologies
1. Auflage 2015
ISBN 978-3-942789-25-7
E.ON ERC Band 27
Rolfs, W.
Decision-Making under Multi-
Dimensional Price Uncertainty
for Long-Lived Energy
Investments
1. Auflage 2015
ISBN 978-3-942789-26-4
E.ON ERC Band 28
Wang, J.
Design of Novel Control
algorithms of Power
Converters for Distributed
Generation
1. Auflage 2015
ISBN 978-3-942789-27-1
E.ON ERC Band 29
Helmedag, A.
System-Level Multi-Physics
Power Hardware in the Loop
Testing for Wind Energy
Converters
1. Auflage 2015
ISBN 978-3-942789-28-8
E.ON ERC Band 30
Togawa, K.
Stochastics-based Methods
Enabling Testing of Grid-
related Algorithms through
Simulation
1. Auflage 2015
ISBN 978-3-942789-29-5
E.ON ERC Band 31
Huchtemann, K.
Supply Temperature Control
Concepts in Heat Pump
Heating Systems
1. Auflage 2015
ISBN 978-3-942789-30-1
E.ON ERC Band 32
Molitor, C.
Residential City Districts as
Flexibility Resource: Analysis,
Simulation, and Decentralized
Coordination Algorithms
1. Auflage 2015
ISBN 978-3-942789-31-8
E.ON ERC Band 33
Sunak, Y.
Spatial Perspectives on the
Economics of Renewable
Energy Technologies
1. Auflage 2015
ISBN 978-3-942789-32-5
E.ON ERC Band 34
Cupelli, M.
Advanced Control Methods for
Robust Stability of MVDC
Systems
1. Auflage 2015
ISBN 978-3-942789-33-2
E.ON ERC Band 35
Chen, K.
Active Thermal Management
for Residential Air Source Heat
Pump Systems
1. Auflage 2015
ISBN 978-3-942789-34-9
E.ON ERC Band 36
Pâques, G.
Development of SiC GTO
Thyristors with Etched
Junction Termination
1. Auflage 2016
ISBN 978-3-942789-35-6
E.ON ERC Band 37
Garnier, E.
Distributed Energy Resources
and Virtual Power Plants:
Economics of Investment and
Operation 1. Auflage 2016
ISBN 978-3-942789-37-0
E.ON ERC Band 38
Calì, D.
Occupants' Behavior and its
Impact upon the Energy
Performance of Buildings
1. Auflage 2016
ISBN 978-3-942789-36-3
E.ON ERC Band 39
Isermann, T.
A Multi-Agent-based
Component Control and
Energy Management System
for Electric Vehicles
1. Auflage 2016
ISBN 978-3-942789-38-7
E.ON ERC Band 40
Wu, X.
New Approaches to Dynamic
Equivalent of Active
Distribution Network for
Transient Analysis
1. Auflage 2016
ISBN 978-3-942789-39-4
E.ON ERC Band 41
Garbuzova-Schiftler, M.
The Growing ESCO Market for
Energy Efficiency in Russia: A
Business and Risk Analysis
1. Auflage 2016
ISBN 978-3-942789-40-0
E.ON ERC Band 42
Huber, M.
Agentenbasierte
Gebäudeautomation für
raumlufttechnische Anlagen
1. Auflage 2016
ISBN 978-3-942789-41-7
E.ON ERC Band 43
Soltau, N.
High-Power Medium-Voltage
DC-DC Converters: Design,
Control and Demonstration
1. Auflage 2017
ISBN 978-3-942789-42-4
E.ON ERC Band 44
Stieneker, M.
Analysis of Medium-Voltage
Direct-Current Collector Grids
in Offshore Wind Parks
1. Auflage 2017
ISBN 978-3-942789-43-1
E.ON ERC Band 45
Bader, A.
Entwicklung eines Verfahrens
zur Strompreisvorhersage im
kurzfristigen Intraday-
Handelszeitraum
1. Auflage 2017
ISBN 978-3-942789-44-8
E.ON ERC Band 46
Chen, T.
Upscaling Permeability for
Fractured Porous Rocks and
Modeling Anisotropic Flow
and Heat Transport
1. Auflage 2017
ISBN 978-3-942789-45-5
E.ON ERC Band 47
Ferdowsi, M.
Data-Driven Approaches for
Monitoring of Distribution
Grids
1. Auflage 2017
ISBN 978-3-942789-46-2
E.ON ERC Band 48
Kopmann, N.
Betriebsverhalten freier
Heizflächen unter zeitlich
variablen Randbedingungen
1. Auflage 2017
ISBN 978-3-942789-47-9
E.ON ERC Band 49
Fütterer, J.
Tuning of PID Controllers
within Building Energy
Systems
1. Auflage 2017
ISBN 978-3-942789-48-6
E.ON ERC Band 50
Adler, F.
A Digital Hardware Platform
for Distributed Real-Time
Simulation of Power Electronic
Systems 1. Auflage 2017
ISBN 978-3-942789-49-3
E.ON ERC Band 51
Harb, H.
Predictive Demand Side
Management Strategies for
Residential Building Energy
Systems
1. Auflage 2017
ISBN 978-3-942789-50-9
E.ON ERC Band 52
Jahangiri, P.
Applications of Paraffin-Water
Dispersions in Energy
Distribution Systems
1. Auflage 2017
ISBN 978-3-942789-51-6
