URBAN TEXTURE ANALYSIS AND ITS RELATION TO BUILDING ENERGY CONSUMPTION

URBAN TEXTURE ANALYSIS AND ITS RELATION TO
BUILDING ENERGY CONSUMPTION





MARCEL IGNATIUS

(B. Eng. Tarumanagara University, M. Sc. (Building Science), NUS)










A THESIS SUBMITTED
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
DEPARTMENT OF BUILDING
NATIONAL UNIVERSITY OF SINGAPORE



2014

ii

ACKNOWLEDGEMENT
First and foremost, I would like to thank God, whose many blessings have made
me who I am today. He has been giving me everything to accomplish this thesis:
patience, health, wisdom, and blessing. There are also several people with whom I
am indebted for their contribution in this research and study.
I would like to express my sincere gratitude to my advisor Prof. Wong Nyuk Hien
for the continuous support, patience, and motivation during my Ph.D study and
research. His guidance, enthusiasm, encouragement, and vast knowledge have
helped me in all the time of research and writing of this thesis. I am forever grateful to
Prof Wong for being an extraordinary supervisor who showed me the road and
helped me started on this post graduate path.
My sincere thanks also goes to Dr. Steve Kardinal Jusuf, for his guidance and
stimulating discussions, and not to mention providing me with many insights on my
research topic. He was always available for my questions and share generously of
his time and vast knowledge.
Also for my colleagues and friends: Adrian Chong, Nedyomukti Imam Syafii, Lee
See Quin, Erna Tan, Terrence Tan, Norish Ishak, Shan Shan, and Anseina Eliza,
who have been helping and supporting me in and out with their research works,
which without them, this study would not see the light of the day. I would also like to
thank Alex Tan, for his effort and time on research and writing guidance, not to
mention reading out my final thesis draft.
I want to convey a great thank you for my colleagues from Center for Sustainable
Asian Cities (CSAC) for those all those lunch and gathering sessions: Ivan, Mizah,
Nazim, Zdravko, Sari, Quyen, Irina, Chelsea, Daniel, Zhang Ji, and Agnes. They
have been such great friends and colleagues, whose encouraging words and
company kept me going when coffee ad lost its stimulating effect.

iii

I would like to express thanks to my beloved parents, Joseph and Sevianti, who
have been giving birth to me at the first place, and providing me with endless
encouragement, motivation, and all of support that I need in whole of my life. I have
been given too much love from you both, which is indispensable and invaluable.
Lastly, and most importantly, I am sending a very special thanks to my amazing
Suri for all her morale support and endless yet tireless care. Her support,
encouragement, quiet patience, and unwavering love were undeniably the bedrock
upon which the past few years. I am forever grateful for having her not just as a great
wife, but also as an excellent mother who has been taking care of our son, Jayden.
Thanks for all your consideration, it helps me getting through my post graduate
years.


iv

TABLE OF CONTENTS
DECLARATION i
ACKNOWLEDGEMENT ii
TABLE OF CONTENTS iv
EXECUTIVE SUMMARY ix
LIST OF TABLES xi
LIST OF FIGURES xiii
NOMENCLATURE xviii
Chapter 1 INTRODUCTION 20
1.1 Research Problem 20
1.1.1 Urbanization and Megacities 20
1.1.2 Climate Change impact on building energy performance in tropics. 21

1.2 Singapore context 22
1.2.1 Geographical overview 22
1.2.2 Singapore Weather and Climate information 23
1.2.3 Tropical climate 23
1.2.4 Singapore Urban Development 24
1.2.5 Building energy consumption in Singapore 25
1.3 Research question 25
1.4 Scope and objectives of research 27
1.5 Organization of study 29
Chapter 2 LITERATURE REVIEW 31
2.1 Introduction 31
2.2 Climate and Built Environment 34
2.2.1 Atmospheric layers and climate zones 34
2.2.2 Urban area in the tropics and solar radiation. 37
2.2.3 Urban Heat Island (UHI) 38
2.2.4 Microclimate condition and urban texture 42
2.3 Urban Texture Variables 43
2.3.1 Early studies on urban form and density parameters 45
2.3.2 Floor Area Ratio 48
2.3.3 Site Coverage 50
2.3.4 “Spacematrix” variables 51
2.3.5 Compactness/Compacity 52
2.3.6 Sky View Factor 34
2.3.7 Sky Exposure Factor 35
2.4 Urban Texture, Microclimate, and Energy Performance 36
2.5 Cooling Load and Heat gains (ASHRAE, 2009) 41
2.5.1 Conduction gain through exterior surfaces 42
2.5.2 Solar Gain through windows 44
2.5.3 Heat Gain from Fresh Air Intake 45
2.6 Analysis tools 45

v

2.7 Summary and Knowledge Gaps 48
Chapter 3 HYPOTHESES and RESEARCH METHODOLOGY 54
3.1 Hypothesis 54
3.2 Methodology 55
3.3 Parametric Study 58
3.4 Scenario Builder 60
3.4.1 “Parent” Scenario 61
3.4.2 “Children” Scenario 62
3.4.3 Scenario naming and examples 67
3.4.4 Obtaining Sky View Factor () 68
3.4.5 Obtaining Sky Exposure Factor () 69
3.5 Local Weather Data 70
3.5.1 Local Outdoor Air Temperature 71
3.5.2 Generating 24-hour temperature profile. 73
3.5.3 Altitude influence on outdoor air temperature 75
3.5.4 Solar Radiation Quantification for Weather Data 77
3.6 Building Simulation 77
3.6.1 Integrated Environmental Solutions Virtual Environment (IES-VE) 78
3.6.2 Boundary condition settings 80
3.7 Final Deliverables 84
3.8 Importance and potential contribution of the research 84
Chapter 4 PRELIMINARY STUDIES 85
4.1 STEVE tool validation on predicting outdoor temperature in urban area. 85
4.1.1 Background and objective 85
4.1.2 Methodology 86
4.1.3 Analysis 88
4.1.4 Results and Analysis 88

4.1.5 Importance for the overall research study 99
4.2 Comparison of STEVE tool and ENVI-met as temperature prediction model. 99
4.2.1 Background and objective 99
4.2.2 ENVI-met and STEVE comparison 99
4.2.3 Methodology 100
4.2.4 Result and discussion 102
4.2.5 Summary 104
4.2.6 Importance for the overall research study 105
Chapter 5 RESULT ANALYSIS AND DISCUSSION 106
5.1 Introduction 106
5.2 Local Temperature Profile Result 106
5.2.1 Local and Background Temperature comparison 106
5.2.2 Energy Simulation Comparison using Local and Background ambient
temperature data. 116
5.3 Simulation Result and Analysis 120

vi

5.3.1 Selected result outputs from IES-VE 120
5.3.2 Overall simulation result from IES-VE 121
5.3.3 Correlation analysis with floor and surface Area 127
5.3.4 Data normalization 128
5.3.5 Normalized data analysis with urban geometry variables 131
5.4 Summary 135
Chapter 6 PREDICTION MODELS DEVELOPMENT 137
6.1 Prediction Model concept 137
6.2 Non-linear Regression Model 138
6.3 Correlation analysis 139
6.4 Thermal load unit regression models development 140
6.4.1 Sensible cooling load unit regression model development 142

6.4.2 Envelope conduction gain unit regression model development 144
6.4.3 Solar gain unit regression model development 145
6.4.4 Fresh air intake gain unit regression model development 147
6.5 Summary and discussions 149
6.6 Models strength and accuracy 153
6.7 Conclusion 156
Chapter 7 SENSITIVITY ANALYSIS 158
7.1 Introduction 158
7.2 Establishing variables limit range 158
7.3 Sensitivity analysis of prediction models 162
7.3.1 Envelope Conduction Gain Unit 163
7.3.2 Solar Gain Unit 169
7.3.3 Sensible Cooling Load Unit 175
7.3.4 Fresh Air Intake Gain Unit 181
7.4 Conclusion 183
Chapter 8 MODEL APPLICATION ON CASE STUDIES 185
8.1 Introduction 185
8.2 Case Study 1 185
8.2.1 Locations 185
8.2.2 Acquiring urban texture variables 187
8.2.3 Thermal load calculation 187
8.3 Case Study 2 190
8.3.1 Site Selection 190
8.3.2 Methodology 191
8.3.3 Design Iterations 193
8.3.4 Thermal load prediction 196
8.3.5 Additional analysis components 200
8.3.5.1 WindanalysisusingVelocityRatio() 200
8.3.5.2 Urbanoutdoortemperatureconditionandgreeneryimplementation 203
8.3.5.3 Outdoorthermalcomfortanalysis 211

vii

8.4 Conclusion 215
8.4.1 Thermal load study 215
8.4.2 Ambient temperature, wind, surface modifications, and energy performance
216
8.4.3 Outdoor Thermal Comfort 218
8.4.4 Conclusions 220
Chapter 9 CONCLUSION 223
9.1 Important findings for each research objective 223
9.1.1 First objective 225
9.1.2 Second objective 227
9.1.3 Third objective 228
9.2 Research contribution 229
9.3 Research limitation 230
9.4 Suggestions for future work and research 230
9.5 Design for the future – density and open space? 231
Chapter 10 PUBLICATION AND CONFERENCE LIST 235
10.1 Conferences 235
10.2 Publications 236
Chapter 11 REFERENCES 237
Chapter 12 APPENDICES 242
12.1 Influence of urban density on air temperature within Singapore central business
district. 242
12.1.1 Background and Objectives. 242
12.1.2 Case Studies 242
12.1.3 Methodology 243
12.1.4 Results 244
12.1.4.1 Temperaturemaximum() analysis 244

12.1.4.2 Temperatureaverage()analysis 246
12.1.4.3 Temperatureminimum()analysis 247
12.1.1 Relationship between urban morphology and air temperature 247
12.1.2 Summary 249
12.1.3 Importance for the overall research study 250
12.2 Parametric study on urban planning model for high density city. 251
12.2.1 Background and objective 251
12.2.2 Methodology 251
12.2.3 Results 253
12.2.3.1 TemperatureMaximum() Map 253
12.2.3.2 TemperatureAverage()Map 254
12.2.3.3 TemperatureMinimum()Map 255
12.2.4 Summary 256
12.2.5 Importance for the overall research study 258
12.3 Table of scenarios for parametric study (per site) 259

viii

12.4 Tabulation of all scenarios (per district scale). 273
12.5 Compilation of monthly dry bulb temperature from Changi MET station (Singapore).
287
12.6 Buildings Envelope Data 288
12.7 ENVI-MET simulation result on different wind speed. 291
12.8 Predicted Temperature Difference between STEVE and ENVI-met 292
12.9 Comparison of STEVE and ENVI-MET temperature. 293
12.10 Local ambient temperature results (STEVE tool results) 294
12.11 Thermal output results, simulated by using Apache under IES-VE. 298
12.12 Matrix table for ECG
U
. 302

12.13 Matrix table for SG
U
. 305
12.14 Energy consumption calculation 307
12.15 Reviewers’ Comment 310




ix

EXECUTIVE SUMMARY
Global urbanization has caused significant increase of urban dwellers. In year
2003, United Nations estimated that by year 2030, up to 5 billion people will live in
urban areas which will be 61% of the world's population. Urbanization brings major
modification on natural landscape; buildings are erected, soil has been transformed
into roads and pavement, greenery has been vastly reduced, etc. The deterioration of
the urban environment through urbanization can be seen from a phenomenon known
as urban heat island (UHI); where cities record higher temperatures in comparison to
their non-urbanized surroundings.
Overheating due to solar radiation and ambient temperature increase in big cities
affect the thermal balance within the environment, where building occupants are
adapting by utilizing air conditioning to achieve comfortable internal condition.
However, on district level, quantification of climatic condition effect on buildings and
vice versa needs further exploration and observation.
A parametric study involves urban texture variation has been conducted to
observe its effect on district energy performance. Thus, these parametric study
scenarios, which focus on non-domestic/office function, are simulated using
Integrated Environmental Solutions (IES) to study the identified urban texture related
to the energy performance, specifically on district cooling load and external heat

gains through building envelopes. This whole process implements weather files that
accounts for the UHI effect to derive models which characterize certain urban texture
along with its related energy performance.
The findings serve to identify the relevant urban texture variables which
characterize urban density and form, such as floor area ratio (FAR), open space ratio
(OSR), story height (ST), gross site coverage, (GSC), and sky view factor (SVF).
Thermal load calculation method is developed to illustrate how combination of
several urban texture variables affects or influences the cooling load and heat gain

x

for the whole precinct. The verification and application of the models were carried out
using a ‘proposed future’ business district. Eventually, these models’ application
produces multiple case studies for benchmarking purpose.
This analysis method will benefit planners particularly at the early design stage,
where microclimatic analysis should be first conducted at the macro level. Hence,
design problems related to high energy usage from any initial design proposals can
be identified and proper adjustments can be made immediately. Moreover, this
approach also ensures planners are well informed regarding their design
implications, especially when the energy study is complemented with other
microclimatic aspect, such as outdoor temperature, greenery planting, urban
ventilation, and thermal comfort. This approach helps to promote good and
environmental friendly designs, where design benchmarking can be made between
various urban designs to select the most compatible design program.

xi

LIST OF TABLES
Table 2.1. The table shows the relationship between FSI, GSI, OSR, and L (Pont and Haupt,
2004). 51


Table 2.2. Urban Texture Variables and their relationship with microclimate studies. 48
Table 2.3. Studies which emphasize the impact of urban texture variables impact on both
microclimate and energy. 50
Table 3.1. Formula table for required width/length determination. 65
Table 3.2. Reference data input for STEVE tool to generate annual temperature profile data.
74
Table 4.1. Boundary Condition for both prediction tool. 100
Table 5.1. Ambient temperature range values for all scenarios, separated in different outputs.
108
Table 5.2. Information on the 11 simulated scenarios. 117
Table 6.1. Thermal load units and their urban texture variables. 137
Table 6.2. Pearson r Correlation Chart. 140
Table 6.3. Prediction model coefficients for log transformed sensible cooling load unit (Log
SCL
u
) with correlation, significance, and collineartity statistics tests under SPSS using
stepwhise method. 143

Table 6.4. Prediction model coefficients for log transformed envelope conduction gain (Log
ECG
U
) with correlation, significance, and collineartity statistics tests under SPSS
using stepwhise method. 145
Table 6.5. Prediction model coefficients for log transformed solar gain unit (Log SG
u
) with
correlation, significance, and collineartity statistics tests under SPSS using stepwhise
method. 146


Table 6.6. Prediction model coefficients for log transformed solar gain unit (Log SG
U
) with
correlation, significance, and collineartity statistics tests under SPSS using stepwhise
method. Variable Log C (compactness) has been excluded. 147

Table 6.7. Prediction model coefficients for log transformed fresh air intake gain unit (Log
FAIG
U
) with correlation, significance, and collineartity statistics tests under SPSS
using stepwhise method. 148

Table 6.8. Interpretation of SCL
U
model in relation to its predictors 149
Table 6.9. Interpretation of ECG
U
model in relation to its predictors 150
Table 6.10. Interpretation of SG
U
model in relation to its predictors 150
Table 6.11. Interpretation of FAIGU model in relation to its predictors 150
Table 6.12. Examples on how to determine the impact of increasing the urban textures
variables on sensible cooling load unit. 151
Table 7.1. Establishing limit range for sensitivity analysis. 159
Table 7.2. SVF limit range values for ECG
U
variables. 161
Table 7.3. SVF limit range values for SG
U

variables. 162
Table 7.4. SVF limit range values for SCL
U
based on various

OSR condition. 162
Table 8.1. Inputs and outputs variables for Case 1 (Shenton Way) 188
Table 8.2. Inputs and outputs variables for Case 2 (Tanjong Pagar) 189
Table 8.3. Urban texture variables values for each iteration. 193
Table 8.4. SVF calculation for each design iteration. 195
Table 8.5. Building surface area for each design iteration. 195
Table 8.6. Thermal load units calculation results. 196

xii

Table 8.7. Predicted total thermal load for each design iteration. 197
Table 8.8. Tabulation of VR independent variables obtained from each design iterations. 202
Table 8.9. Tabulation of VR and wind speed values for each design iteration. 202
Table 8.10. Outdoor temperature results from STEVE tool on various scenarios. 208
Table 8.11. Tabulation of outdoor temperature impact on energy consumption reduction. 210
Table 8.12. TSV index categories of outdoor thermal comfort. 212
Table 8.13. Tabulation of outdoor temperature and wind impact on outdoor thermal comfort,
represented with TSV index. 214
Table 12.1. Matrix of different building configurations on each block. 252



xiii

LIST OF FIGURES

Figure 1.1. Köppen - Geiger climate type of Asia (Source: -
wien.ac.at/present.htm) 22

Figure 1.2. Two extreme urban form scenarios which lead to the research question for this
study. 27
Figure 2.1 Interactions between physical constituents and biological (living) constituents
(Yeang, 1995) 31
Figure 2.2. Urban microclimate analysis diagram looking at different aspects. 32
Figure 2.3. Literature review focuses on the relationship between 3 main subjects: urban
texture, microclimate, and heat gain/energy. 33

Figure 2.4 Schematic representation of the urban atmosphere illustrating a two-layer
classification of urban modification (Oke, 1987) 34
Figure 2.5. Universe class division into different thermal climate zones (Stewart and Oke,
2009b) 35

Figure 2.6. Thermal Climate Zones – City Series (Stewart and Oke, 2009b) 36
Figure 2.7. Schematic of the earth’s energy budget (Schneider, 1992) 38
Figure 2.8. Urban heat island characteristics (Voogt, 2004). 39
Figure 2.9. UHI profile in Singapore (Wong and Chen, 2003) 41
Figure 2.10. Relationship diagram between urban texture, microclimate, and building energy
usage. 42
Figure 2.11. Two archetypal urban patterns, based on pavilions and courts (black represents
buildings) with the same site coverage, building height and total floor space (Martin
and March, 1972). 46

Figure 2.12. Fresnel’s diagram: all concentric squared annuluses have the same surface
area, which is also equal to the area of the centre square (Martin and March, 1972) 47
Figure 2.13. Generic urban forms, based on Martin and March and environmentally. From left
to right: pavilions, slabs, terraces, terrace-courts, pavilion-courts and courts (Martin

and March, 1972). 47

Figure 2.14. Leslie Martin and Lionel March’s (1972) radical proposal to replace a part of
central Manhattan with large courts. 47

Figure 2.15. Plot ratio as one of measures of building density (Cheng, 2009) 50
Figure 2.16. Site coverage indicator, another measure of building density (Cheng, 2009) 50
Figure 2.17. Digital Elevation Model (DEM) of London, Toulouse and Berlin, with heights
represented with a 256-level grey scale (Ratti et al., 2005) 33

Figure 2.18. The sky view factor (SVF) is a measure of the street geometry and the photos
shows three sites of (a) open square, SVF=0.93); (b) street intersection, SVF=0.47
and (c) street canyon, SVF=0.29 (Eliasson, 2000) 34

Figure 2.19. Sky Exposure Factor (left) and Sky View Factor (right) (Johnson and Watson,
1984). 36

Figure 2.20. Sky view factor horizontal distribution over Tokyo. I, II, and III denote different
urban canopy type (Kikegawa et al., 2006; Kikegawa et al., 2003). 37
Figure 2.21. Urban energy consumption studies with LT method, which incorporates several
form descriptors and heat gains (Ratti et al., 2005). 39

Figure 2.22. Factors that affect energy consumption in buildings; according to Baker and
Steemers, building design accounts for a 2.5X variation, system design and
occupants behaviour for a 2X variation each; the contribution of the urban context is
not quantified (Baker and Steemers, 1992; Baker and Steemers, 2000; Ratti et al.,
2005). 39


xiv

Figure 2.23. Schematic of heat balance processes in a zone (ASHRAE, 2009). 43
Figure 2.24. Thermal circuit for conduction process through wall 43
Figure 2.25. Common workflow in building thermal or energy simulation. 46
Figure 3.1. Overall research methodology workflow. 56
Figure 3.2. Research workflow on parametric study. 57
Figure 3.3. The sample crop of Singapore CBD master plan (source: Google maps). 60
Figure 3.4. Compilation of all 'parent' scenarios, with site coverage and height increments. . 61
Figure 3.5. Scenario builder flowchart by using ‘Parent’ and ‘Children’ concept. 63
Figure 3.6. Schematic view on placing grid and how to determine building dimension for
‘Children’ scenarios. 64
Figure 3.7. Typical neighborhood setting for all scenarios. 65
Figure 3.8. A complete set of parent (type 1) and its children scenarios (type 2 to 12). 66
Figure 3.9. Scenario examples which has the same plot ratio of 3. 67
Figure 3.10. Scenario examples which has the similar surface area of ±30,000 sq m. 68
Figure 3.11. Screen shot of SKYHELIOS software to obtain SVF values of an urban area
(Matuschek and Matzarakis, 2010) 69
Figure 3.12. Measurement points deployment (total 39 points) on each scenario, to acquire
local temperature data. 75
Figure 3.13. IES-VE is capable to perform simulations on different aspect (IES, 2010) 78
Figure 3.14. Simulation worfklow under IES-VE for the purpose of this study (IES, 2010) 79
Figure 3.15. Screenshot of simulated building within IES-VE 80
Figure 3.16. Screen shot of solar shading calculation under SunCast. 80
Figure 3.17. Weekdays and weekend schedules for occupancy, lighting, equipment and
infiltraton profiles. 81

Figure 3.18. Wall construction material. 82
Figure 3.19. Glazing construction material. 83
Figure 3.20. Roof construction material. 83
Figure 4.1. Measurement points at Shenton Way. 87

Figure 4.2. Measurement points at Tanjong Pagar. 88
Figure 4.3. HOBO U12-011 (left), Solar Shield mounted onto lamp post (Middle, Right). 88
Figure 4.4a-k. Modeled versus measured 24 hour temperature profile averaged for March . 93
Figure 4.5a-k. Modeled versus measured 24 hour temperature profile averaged for March 97
Figure 4.6. Measured and predicted temperature against measured temperature for March
and April 2012. 97
Figure 4.7. Box plot showing 3
rd
quartile, median and 2
nd
quartile with mean of measured and
predicted data. 98

Figure 4.8. Model settings for STEVE (left, using GIS) and ENVI-met (right). 100
Figure 4.9. Additional buffer zone on STEVE calculation to improve the temperature map
resolution. 101
Figure 4.10. Temperature profile comparison chart. 102
Figure 4.11. Comparison of STEVE and ENVI-MET Temperature chart. 103
Figure 5.1. Box plot of different temperature outputs compared with the data recorded at
weather station. 107

Figure 5.2. Scatter plot of ambient temperature outputs and sky view factor. 108

xv

Figure 5.3. Maximum Temperature chart for all scenarios (above) and scenarios with 30% site
coverage (below). 111
Figure 5.4. Average Temperature Daytime chart for all scenarios (above) and scenarios with
30% site coverage (below). 112


Figure 5.5. Average Temperature chart for all scenarios (above) and scenarios with 30% site
coverage (below). 113
Figure 5.6. Minimum Temperature chart for all scenarios (above) and scenarios with 30% site
coverage (below). 114
Figure 5.7. Temperature values range band for different outputs. 115
Figure 5.8. Eleven random scenarios, which were simulated using two different sets of
temperature data. 117

Figure 5.9. Charts showing different thermal components output by comparing local and
background ambient temperature implementation. 119
Figure 5.10. Composition of sensible cooling load. 121
Figure 5.11. Sensible Cooling Load Charts with information on floor and surface area. 123
Figure 5.12. Envelope conduction gain chart with information on floor and surface area. 124
Figure 5.13. Solar gain chart with information on floor and surface area. 125
Figure 5.14. Fresh air intake gain chart with information on floor and surface area. 126
Figure 5.15. Scatter plot of cooling load and heat gain outputs with their respective floor area.
Pearson’s r coefficient was used to indicate the relationship between variables. 127

Figure 5.16. Scatter plot of cooling load and heat gain outputs with their respective surface
area. Pearson’s r coefficient was used to indicate the relationship between variables.
128

Figure 5.17. Three selected scenarios with similar GFA and surface area. 129
Figure 5.18. Identified urban geometry variables to be correlated with simulation outputs
(Cheng, 2009; Ji et al., 2011; Pont and Haupt, 2004, 2010; Salat, 2011) 130

Figure 5.19. Scatter plots showing correlation between normalized thermal output data
(cooling load and external heat gains) with selected urban geometry variables) 132
Figure 6.1. Calculation diagram for prediction models. 137
Figure 6.2. Scatter plots display the correlations between log transformed thermal unit loads

with log transformed urban texture variables. 141
Figure 6.3. Scatter plot comparing results of predicted and simulated thermal load units. 153
Figure 6.4. Comparison between simulated and predicted values for each thermal load units.
154

Figure 6.5. Box plot showing 3rd quartile, median and 2nd quartile with mean of simulated
and predicted data. 155

Figure 6.6. Final prediction work flow to determine thermal load of a precinct. 157
Figure 7.1. Grid arrangement with 300 x 300 m boundary size to establish limit range for
workable variables. 159

Figure 7.2. Illustration showing the difference between type 1 and 2 in term of compactness,
while maintaining the other parameters (OSR, GSC, FAR, and ST). 160
Figure 7.3. Sensitivity analysis charts for ECG
U
164
Figure 7.4. Example set layout of configurations with SVF variation, and everything else is
fixed. 165

Figure 7.5. Example set layout of configurations with ST variation, and everything else is
fixed. 166

Figure 7.6. Sensitivity analysis showing the range values for various OSR groups. 168
Figure 7.7. Sensitivity analysis charts for SG
U.
170

xvi

Figure 7.8. Example set layout of configurations with FAR variation, and everything else is
fixed. 171
Figure 7.9. Chart showing the relationship between SGU and Area/Perimeter Ratio under
fixed SVF condition. 173

Figure 7.10. Example set layout of configurations with SVF variation, and everything else is
fixed. 174
Figure 7.11. Sensitivity analysis charts for SCL
U
. 175
Figure 7.12. Example set layout of configurations with SVF variation, and everything else is
fixed. 177
Figure 7.13. Example set layout of configurations with SVF, ST, FAR, and GSC variation, and
OSR was fixed at 0.5. 178

Figure 7.14. Example set layout of configurations with OSR variation and SVF was fixed at
0.65-0.68 180
Figure 7.15. Sensitivity analysis charts for FAIG
U
. 181
Figure 7.16. Scatter plot showing the correlation between fresh air intake gain unit (FAIG
U
)
with sky view factor (SVF), maximum temperature (T
max
), and average temperature
(T
avg
). 182
Figure 8.1. Selected areas for first case study. 186

Figure 8.2. Master plan data provides buildings information (source: URA). 186
Figure 8.3. Sky View Factor map calculated with Skyhelios. 188
Figure 8.4. Marina Bay has been chosen as the studied area to demonstrate the models
application. 190
Figure 8.5. Precinct design guideline (source: URA) 190
Figure 8.6. Model application and other micro climatic analysis studies work flow. 192
Figure 8.7. Parametric design results using site coverage, compactness, and building height
iterations. 194

Figure 8.8. Design iterations which have the lowest annual ECG, SG, SCL (top) and FAIG
(bottom). 199
Figure 8.9. Filtered out design iterations comprises cases with low thermal loads. 200
Figure 8.10. Both 9 ha and 25 ha are the boundary area to calculate thermal load unit and VR
respectively. 201
Figure 8.11. Outdoor temperature surface and wind iterations. 204
Figure 8.12. Open space modifications which will be applied on each design iteration to
observe its impact on ambient temperature. 205

Figure 8.13. Calculating outdoor temperature without trees influence. 205
Figure 8.14. Calculating outdoor temperature with trees considered. 206
Figure 8.15. Scatter plot displaying district energy performance (kWh/m
2
/yr) compared
against the ambient temperature condition with regards of wind and various ground
surface treatments. 216

Figure 8.16. Scatter plot displaying district energy performance (kWh/m
2
/yr) compared
against the outdoor thermal comfort (TSV index) with regards of wind and various

ground surface treatments. 219

Figure 8.17. Overall compilation of energy performance, ambient temperature, and outdoor
thermal comfort from various design iterations. 220

Figure 9.1. Overall research diagram. 224
Figure 9.2. Assessment method on observing energy performance in district/precinct level.
226
Figure 9.3. Design and engineering process loop. 230

xvii

Figure 9.4. Outdoor activities in urban centres. 233
Figure 9.5. Some examples of radical architectural design which elevate the density upwards,
creating enough space below for public activities. (Source: 1. Viktor Ramos, Richie
Gelles, 2. Aprilli Design studio, 3. and 4. Office of Metropolitan Architecture). 233

Figure 12.1. Methodology diagram. 243
Figure 12.2. Maximum temperature map and profiles of maximum temperature against Sky
View Factor and Green Plot Ratio. 245
Figure 12.3. Average temperature map and profiles of average temperature against Sky View
Factor and Wall Surface Area. 245
Figure 12.4. Minimum temperature map and profiles of minimum temperature against Sky
View Factor and Wall Surface Area. 246

Figure 12.5. Scatter plot of Tanjong Pagar area predicted temperature correlated with
different parameters. 248
Figure 12.6. Scatter plot of Robinson Road and Shenton Way area predicted temperatures
correlated with different parameters. 249


Figure 12.7. Scatter plot of CBD (Tanjong Pagar, Robinson Road, and Shenton Way) area
predicted temperatures correlated with different parameters. 249

Figure 12.8. Selected 7 blocks in commercial district with plot ratio 11.2. 251
Figure 12.9. Types of different building configuration located on 7 blocks in Singapore's
commercial district. 252
Figure 12.10. T
max
temperature map on existing site condition compared with 6 scenarios of
urban configuration and density 254

Figure 12.11. T
avg
temperature map on existing site condition compared with 6 scenarios of
urban configuration and density. 255
Figure 12.12. T
min
temperature map on existing site condition compared with 6 scenarios of
urban configuration and density. 256
Figure 12.13. Average temperature difference on massing configuration scenarios 257




xviii

NOMENCLATURE
ALB Average surface albedo
A Area of aggregation (m
2

)
ANSI American National Standards Institute
ASHRAE American Society of Refrigerating and Air-conditioning Engineers
B Gross building footprint (m
2
)
BCA Building and Construction Authority, Singapore
BS Building Shape
C Volumetric Compactness
CBD Central Business District
CF Correction factor of solar heat gain through fenestration
CMH Cubic meter per hour
C
f
Compacity Factor
CIBSE The Chartered Institution of Building Services Engineers
DSE Design system efficiency
ECG annual Envelope Conduction Gain (MWh/year)
ECG
U
Envelope Conduction Gain Unit (Wh/m
4
)
EPW Weather data file format
ETTV Envelope Thermal Transfer Value (W/m
2
)
FAIG annual Fresh Air Intake Gain (MWh/year)
FAIG
U

Fresh Air Intake Gain Unit (Wh/m
4
)
FAR Floor Area Ratio
GBCR Gross Building Coverage Ratio
GDP Gross domestic product
GEO Geometry
GFA Gross Floor Area (m
2
)
GIS Geographic Information System
GnPR Green Plot Ratio
GSC Gross site coverage
HBDG Average height to building area ratio
HV Building Height Variation
H
z
Geopotential Altitude
IES-VE Integrated Environmental Solutions Virtual Environment: building
performance simulation tools
IPCC Intergovernmental Panel on Climate Change
K
d
Ratio of diffuse radiation to global radiation
K
t
Ratio of global radiation to extraterrestrial radiation
Macro level Estate level
MET Meteorological
N Number of days in month m

NEA National Environment Agency
ORIENT Orientation
OSR Open Space Ratio
OTTV Overall Thermal Transfer Value
PAVE Percentage of pavement area over R 50m surface area (%)
PERM Permeability
R
2
Coefficient of determination
RefT
avg
daily average temperature at reference point
RefT
max
daily maximum temperature at reference point
RefT
min
daily minimum temperature at reference point
RETV Roof Thermal Transfer Value
SC Site Coverage (%)
SCL annual Sensible Cooling Load (MWh/year)
SCL
U
Sensible Cooling Load Unit (Wh/m
4
)

xix

SG annual Solar Gain (MWh/year)

SG
U
Solar Gain Unit (Wh/m
4
)
SkyEF Sky Exposure Factor
SOLAR average of daily solar radiation
SOLAR
max
average of solar radiation maximum of the day
SOLAR
total
average of daily solar radiation total
ST Storey height
STAG Staggering of Blocks Arrangement
STEVE Screening Tool for Estate Environment and Evaluation
SVF Sky view factor
T
avg
Average outdoor air temperature v(
o
C)
T
avg (daytime)
Average daytime outdoor air temperature (
o
C)
T
avg (night time)
Average night time outdoor air temperature (

o
C)
T
b
Air temperature at base (ground) (
o
C)
T
bt
Air temperature at bottom with tree influence (
o
C)
T
max
Maximum outdoor air temperature (
o
C)
T
min
Minimum (night time) outdoor air temperature (
o
C)
TSV Thermal Sensation Vote
T
z
Air temperature at altitude z (
o
C)
UC Map Urban Climatic Map
U

f
thermal transmittance of fenestration (W/m
2
°K)
UHI Urban Heat Island
U-value Thermal transmittance
U
w
thermal transmittance of opaque wall (W/m
2
°K)
V Wind speed (m/s)
VAV Variable Air Volume
VR Ventilation Ratio
WALL total wall surface area
WIND
max
Wind speed at the time of occurrence of Ref T
max

WMO World Meteorological Organization
WWR Window-to-Wall Ratio (fenestration area / gross area of exterior wall)
X
avg
Average temperature recorded at meteorological station respectively
during month m
X
d,t
Air temperature recorded by the meteorological station on day d and hour
t

X
max
Maximum temperature recorded at meteorological station respectively
during month m
X
min
Minimum temperature recorded at meteorological station respectively
during month m
<X
d,t
>
m
Variable X
d,t
averaged over the number of days N for each hour t in
month m, generating a 24 hour profile of averages

Azimuth Angle

Elevation Angle




20

CHAPTER 1 INTRODUCTION
1.1 Research Problem
1.1.1 Urbanization and Megacities
The world has experienced unprecedented urban growth in the last and current

centuries. In 1800, only 3% of the world’s population lived in urban areas. It
increased to 14% and 47% in 1900 and 2000 respectively. Since 2008, for the first
time in history, more than half of the world population lives in the urban areas (Laski
and Schellekens, 2007). In year 2003, United Nations estimated that by year 2030,
up to 5 billion people will be living in urban areas accounting for 61% of the world's
population.
The on-going migration to urban areas has massive environmental consequences.
This condition of unprecedented shift from the countryside to cities has been
influencing climate change, where urban areas account for up to 70% of the world
greenhouse gas emissions. Since half of the world population lives in the tropics
(EIU, 2011), including Singapore, significant attention should be paid to urban
context within the tropics.
Cities are growing towards megacities with higher density urban planning,
narrower urban corridors and more high-rise urban structures. Increasing
urbanization causes the deterioration of the urban environment, as the size of
housing plots decreases, thus increasing densities and crowding out greeneries
(Santamouris et al., 2001a). Within the built environment at micro-scale, buildings
and vegetation influence the incident solar radiation received by urban surface.
Cities tend to record higher temperatures than their non-urbanized surroundings,
a phenomenon known as Urban Heat Island (UHI) (Jusuf et al., 2007; Oke, 1982).
Earlier studies show strong relation between urban morphology and increasing air
temperature within city centers. Urban structures absorb solar heat during the day
and release it during the night. Densely built area tends to trap heat which is released

21

from urban structures into the urban environment, increasing urban air temperature
compared to surrounding rural areas and causes UHI effect. UHI affects street level
thermal comfort, health, environment quality, and may increase the urban energy
demand.

However, a more concerning matter is the changing of the earth climate, which
has been anticipated to have strong implications on building sector.
1.1.2 Climate Change impact on building energy performance in tropics.
The third assessment report of the Intergovernmental Panel on Climate Change
(IPCC) summarized the implications of climate change on the building sector as
“increased electric demand and reduced energy supply reliability” (IPCC, 2002).
Global warming has been predicted to have strong impact on building energy
performance, since the usage of heating and cooling are highly related to both
temperature and weather variations.
Building sector is accountable for more than 40% of global energy consumption
and 30% of global greenhouse emissions, which comes from both commercial and
residential usage. Among the factors that contribute to the buildings’ emissions are
building design, building envelope, on-site distributed generation, energy end use in
the building, lighting, air-conditioning, space heating and ventilation (C2ES, 2009).
In the ASEAN region, commercial buildings are accountable for 30% of all the
electricity use and will demand approximately another 40% of generation capacity in
years to come (MECM, 2001). Numerous studies have been conducted to predict
commercial building energy consumption. Currently, increasing demand for
appropriate thermal comfort during hot summer leads to the increase in building
energy consumption (Lam et al., 2008).
In spite of these findings, the buildings sector has the greatest potential to mitigate
the impact of global warming, and the development of adaptation and mitigation
strategies has become a major challenge for building professionals. Hence, those

22

strategies are dependable on well-developed prediction models in order to
investigate the performance of buildings in a future warmer climate.
1.2 Singapore context
1.2.1 Geographical overview

Geographically, Singapore is located between latitudes 1°09' North and 1°29'
South, longitudes 103°36' East and 104°25' East. Köppen climate classification
system, as shown in Figure 1.1, identifies tropical climate as a region climate which
covers the largest area of earth, approximately 20% of land surface and 40% of
ocean surface. Singapore is identified within Af coded region. Af region stands for
tropical rainforest climate which has characteristics of high humidity with daily
temperature range of 10° to 25°C and minimum precipitation of at least 60 mm all
year around.

Figure 1.1. Köppen - Geiger climate type of Asia (Source: -
wien.ac.at/present.htm)


23

1.2.2 Singapore Weather and Climate information
Located right in the equator line, Singapore features a tropical climate, which is
fairly constant throughout the year; with hot and humid weather in most of the months
and very minor variation in temperatures. According to Singapore Changi airport
weather station, the maximum outdoor temperature in 2010 varies between 29
o
C -
31
o
C, with minimum between 23
o
C-24
o
C (NEA, 2009).
There are two main seasons in Singapore, Northeast (NE) Monsoon and the

Southwest (SW) Monsoon. The NE monsoon occurs between November and early
March with the prevailing wind blowing from the North and Northeast. Meanwhile, the
SW monsoon occurs between June and September with prevailing wind from the
South and Southwest. Two short inter-monsoon periods with the duration of two
months separate these main seasons.
There is no clear distinct wet or dry season as rainfall occurs throughout the year.
However, NE monsoon season is considered as wet weather, since the wind is
generally cool and bring frequent spell of wet weather, about 48% of the total annual
rainfall. On the other hand, the SW monsoon wind brings about 36% of the total
annual rainfall.
1.2.3 Tropical climate
High temperature and relative humidity are the most straightforward
characteristics of tropical region, which may cause thermal discomfort. Singapore’s
hot and humid climate is also characterized by small seasonal variations; due to its
low latitude of 1.37°. Furthermore, heat loss at night is limited by the abundant cloud
cover, in which temperature at night is still relatively high (Wong and Chen, 2009b).
Since Singapore is located close to the equator, overheating due to solar radiation
occurs all year round, making them undesirable. Generally, solar radiation receivable
in the tropics is very high, especially the proportion of diffuse radiation. This is mainly
due to the high humidity and cloud cover in the region.

24

The excessive amount of solar radiation received by buildings can affect interior
thermal conditions. Solar radiation enters the buildings through fenestrations and
heat up the interiors. Additionally, heat absorption by building façades is transferred
to the interior through conduction, which indirectly could increase the indoors heat
gain.
In general, the hottest month in Singapore based on the annual climatic data
occurs in March. Based on the 2010 Annual Weather Review released by National

Environmental Agency (NEA, 2010), the highest temperature on 2010 was recorded
on 6 March 2010, where the dry bulb temperature at the climate station reached a
maximum of 35.5°C. This made it one of the 5 hottest days on record since 1983.
This month sees the transition of the Northeast Monsoon into the Inter-monsoon
Period. Surface wind becomes increasingly light and showers/thunderstorms more
frequent in the later part of the month. March may be noted for its hot afternoons;
light winds, cloudless skies and more direct solar radiation.
1.2.4 Singapore Urban Development
Singapore, as the most developed country within Southeast Asia, has been
experiencing rapid urban development. A rapid growth of urbanization had
transformed its 1.8 million populations in year 1965 to 5.3 million population in year
2013. Singapore is ranked third in terms of the world’s most densely populated
countries with more than 7 thousand population/km
2
, based on Singapore statistics in
year 2013 (DOS, 2013).
On the contrary of socio-economic growth and its positive impacts on the
globalized transformation of cities morphology and identity, urbanization influences a
change of cities profile and land usage composition which convert more open spaces
to high density building blocks. To maintain the country's strong economic growth, its
commercial district is one of the highly developed areas, which allows higher building
site coverage and plot ratio with rows of high-rise buildings for residential and