An Optimization Approach for Integrating Different Roof Functions with Environmental Impacts Constraint: "A Hybrid Framework".

ABSTRACT

NOOSHAFARIN, MOHAMMADZADEH. An Optimization Approach for Integrating Different Roof Functions with Environmental Impacts Constraint: “A Hybrid Framework”. (Under the direction of Dr. Soolyeon Cho)

The roof, as part of the building envelope, is one of the most important areas in sustainable

building development. Various roof design strategies have been developed for reducing

building energy usage, generating energy, improving water retention, and waterproofing.

These strategies are actively utilized due to a vast amount of interest in maximizing the

utilization of building spaces, including roof area. However, challenges to finding integral

optimal solutions include sustainability, funding, and environmental recognition.

Although sustainable roof design technologies have been utilized and improved in prior roof

design efforts, they are mostly focused on a single technology, such as green roof, cool roof,

or solar energy generation by PV panel installation. It is quite obvious that if more than one

sustainable roof design strategy can be applied in a contingent way, additional valuable effects

can be achieved. This requires creating a quantitative relationship among the spectrums of roof

performance factors.

Integrating different roof functions is of interest to building practitioners in order to meet

certain criteria in terms of energy efficiency, cost effectiveness, and environmental impact.

This research presents an enhanced roof system and framework that integrates multiple roof

functions and optimizes desired roof performance.

The proposed framework contains a mathematical optimization model as a core, with inputs

measurements. Energy analysis, which was carried out through roof energy simulations, plays

a major role in this framework. The proposed framework incorporates various roof design technologies’ quantitative analysis results on energy and cost savings with respect to

environmental impacts. The findings of this research have been implemented in a

computational engine developed in the Excel/VBA environment. From a theoretical

standpoint, a set of diverse inputs have been fed into the model to validate the resulting outputs.

These further tests verify the framework’s faithful implementation. To explore the climate

impact and identify design parameters that significantly impact energy and environment,

simulations of a prototype office building are conducted for U.S. cities in different climate

conditions. In all buildings, energy simulations for different roof functions and conventional

© Copyright 2016 Nooshafarin Mohammadzadeh

An Optimization Approach for Integrating Different Roof Functions with Environmental Impacts Constraint: “A Hybrid Framework”

by

Nooshafarin Mohammadzadeh

A dissertation submitted to the Graduate Faculty of North Carolina State University

in partial fulfillment of the requirements for the Degree of

Doctor of Philosophy

Design

Raleigh, North Carolina 2016

APPROVED BY:

Dr. Soolyeon. Cho, Committee Chair

Dr. Jianxin Hu,

ii

DEDICATION

I dedicate this dissertation to my beloved parents, Soheila Lashani and Hossein

Mohammadzadeh and my beloved sisters Nilufar & Nazanin Mohammadzadeh, for their

unwavering love, support and encouragement.

I also dedicate this dissertation to my lovely family who have supported me throughout this

long-term study process.

I would also like to thank my best friends in Raleigh, Dr. Hamid Kazem, Farshad Ramizi,

Dr. Behzad Aghdashi, Samaneh Fakhimi, Adele Moatti, Mahboubeh Ameri, Simin Imanirad,

Dr. Morteza Ashouri, Dr. Aliashghar Marjani, Pegah Pooya and Amir Ghanbari. They

encouraged and supported me during period of my PhD studies, and I have lots of good

memories with them.

iii

BIOGRAPHY

Nooshafarin Mohammadzadeh received her Master’s degree in Architecture from International

University of Ghazvin and Bachelor’s degree in Architectural engineering from Esfahan Azad

University, Iran. She joined Building Energy Technology Lab (BETlab) at North Carolina

State University (NCSU) at 2012. As a graduate research assistant, she contributed in

high-performance building design, energy-efficient systems design, renewable energy systems

integration, thermal comfort analysis and building energy optimization. Noosha has conducted

extensive research on the integration of roof systems and energy analysis in buildings

iv

ACKNOWLEDGMENTS

I would like to thank my advisor and committee chair Dr. Soolyeon Cho for the extensive

guidance, expertise, constant support and advice he has given me throughout my graduate

program. A special thank you goes to Professor Art Rice for kindly accepting to be a member

of my advisory committee. Thank you to Dr. Timothy Johnson for his guidance valuable

support and expertise in many facets of this research program. Thanks you to Dr. Jianxin Hu

for his constructive comments.

Thank you to my family and friends for giving me support and encouragement in all that I do.

Finally, I extend my heartfelt gratitude to my parents for all that they have done for me.

v

TABLE OF CONTENTS

LIST OF TABLES ... vii

LIST OF FIGURES ... viii

LIST OF SYMBOLS ... xii

Chapter 1 INTRODUCTION...1

Research Overview ... 1

Research Questions ... 2

Research Goal ... 3

Objectives of Research ... 4

Chapter 2 LITERATURE REVIEW ...5

State of The Art ... 5

Integrated Roof Systems ... 5

Definition of terms ... 5

Energy Performance ... 5

Building Simulation programs ... 8

Spectral Properties of Solar Radiation ... 10

Cost Analysis ... 13

Environmental Performance ... 14

Roof system technologies ... 15

Literature Review on Roof systems ... 36

Details of Findings from Previous Work ... 39

Studies of Existing tool for Roof Functions ... 43

Findings about existing tools: ... 49

Summary of literature review ... 50

Chapter 3 METHODOLOGY ... 52

Conceptual Framework ... 52

Assumptions ... 55

Theoretical Aspects of Engineering Economic Analysis ... 56

NPV Analysis ... 56

Optimization Model ... 61

Decision variables: ... 61

Objective Function ... 61

Variables Definitions ... 63

vi

Mathematical Model’s Parameters Estimation ... 70

Chapter 4 VERIFICATION AND VALIDATION ... 100

Implementation (Tool Development) ... 100

Preliminary Energy Simulation ... 103

Maximum necessary percentage of PV system ... 109

Numerical Results: ... 123

Numerical results for a one-story building (Fairbanks, Las Vegas, Los Angeles) ... 124

Case Study: Duke Environment Hall ... 149

Environment Hall green Features... 150

Numerical Results of Environment Hall: ... 153

Sensitivity analysis ... 163

Chapter 5 SUMMARY, CONCLUSIONS, AND FUTURE WORK ... 169

REFERENCES ... 174

APPENDICES ... 187

Appendix 1: TOOL DEVELOPMENT ... 188

Appendix 2: ANALYSIS RESULTS OF 16 REPRESENTATIVE CITIES: ... 195

1 Story Building: ... 195

vii

LIST OF TABLES

Table 2-1: Typical minimum cool roof requirement, California energy commission ... 19

Table 2-2: Standard course depths for different types of roof-greening [65] ... 23

Table 2-3: Different types of green roof based on LAI and soil depth [66] ... 24

Table 2-4: Summary of existing tools... 45

Table 2-5: Different roof functions ... 48

Table 3-1: Different types of green roof based on LAI and soil depth [66] ... 73

Table 3-2: Different types of Cool roofs ... 74

Table 3-3: Different types of PV systems [77] ... 74

Table 3-4: Summary of recent solar PV installed system costs [77] ... 75

Table 3-5: Business Energy Investment Tax Credit [146] ... 77

Table 3-6: PV installation cost ... 78

Table 3-7: Installation costs, maintenance costs, energy reduction obtained from literature ... 79

Table 3-8: CO2 emissions from U.S. electricity generation by source, 2014, [158] ... 95

Table 3-9: Gas Carbon Equivalent [39] ... 96

Table 3-10: Carbon footprint calculator ... 97

Table 3-11 Emission Factor ... 98

Table 4-1: Energy per conditioned building area– 1 story building (Albuquerque to Helena) ... 105

Table 4-2: Energy per conditioned building area– 1 story building (Houston to Seattle) ... 106

Table 4-3: Energy per conditioned building area– 2 story building (Albuquerque to Helena) ... 107

Table 4-4: Energy per conditioned building area– 2 story building (Houston to Seattle) ... 108

Table 4-5: 16 representative cities ... 123

Table 4-6: Percentage of different roof types as a function of different environmental impacts ... 132

Table 4-7: Percentage of different roof types as a function of different environmental impacts ... 140

Table 4-8: Percentage of different roof types as a function of different environmental impacts ... 148

Table 4-9: Percentage of different roof types as a function of different environmental impacts ... 161

viii

LIST OF FIGURES

Figure 2-1: Atmospheric solar spectra [36] ... 11

Figure 2-2: Grid-connected photovoltaic system [40]... 13

Figure 2-3: a) Spectral solar power distribution, (b) Solar spectral reflectance of cool and standard brown surfaces [51] ... 20

Figure 2-4: Temperature distribution of the green roof at a given diurnal time [68] ... 26

Figure 2-5: PV systems Components [75] ... 31

Figure 2-6: PV Arrays Are Composed of Modules that Are Composed of Cells [75] ... 32

Figure 2-7: PV Material [77] ... 34

Figure 2-8: Intensity of sunlight in mid-day in June and December [86] ... 36

Figure 2-9 : Summary of key findings from previous studies ... 38

Figure 3-1: Conceptual framework ... 53

Figure 3-2: High level analysis framework... 54

Figure 3-3: Uniform series of cash flow [135] ... 57

Figure 3-4: Cash flow diagram involving a positive uniform gradient [136] ... 58

Figure 3-5: Geometric Gradient based cash flows [137] ... 58

Figure 3-6: Future to present value [138] ... 59

Figure 3-7: Average Electricity Cost to the Ultimate Customers (Cents per Kilowatt hour) [141] ... 71

Figure 3-8: energy profile of Florida [142] ... 72

Figure 3-9: PV power costs ($/Wp) as function of module efficiency and areal cost [144] ... 75

Figure 3-10: Cost of PV panel from 1995 to 2020 [77, 145] ... 76

Figure 3-11: Cell efficiencies improvement ... 82

Figure 3-12: Calculation procedure for EnergyPlus simulation ... 84

Figure 3-13: Annual heat balance of a green roof for varying leaf albedos [73] ... 86

Figure 3-14: Heat gain through roof for varying Leaf Area Index [73] ... 88

Figure 4-1: The main page of RFO in Excel/VBA environment... 100

Figure 4-2: Second page of RFO in Excel/VBA environment ... 101

Figure 4-3: Third page of RFO in Excel/VBA environment ... 102

Figure 4-4: Optimization calculation MATLAB script ... 103

Figure 4-5: Electricity need and electricity generation for San Francisco ... 111

Figure 4-6: Electricity need and electricity generation for San Francisco ... 111

Figure 4-7: Electricity need and electricity generation for San Francisco ... 111

ix

Figure 4-9: Electricity need and electricity generation for Minneapolis ... 112

Figure 4-10: Electricity need and electricity generation for Miami ... 112

Figure 4-11: Electricity need and electricity generation for Los Angeles ... 113

Figure 4-12: Electricity need and electricity generation for Las Vegas ... 113

Figure 4-13: Electricity need and electricity generation for Helena ... 113

Figure 4-14: Electricity need and electricity generation for Fairbanks ... 114

Figure 4-15: Electricity need and electricity generation for Duluth ... 114

Figure 4-16: Electricity need and electricity generation for Denver ... 114

Figure 4-17: Electricity need and electricity generation for Chicago ... 114

Figure 4-18: Electricity need and electricity generation for Baltimore ... 115

Figure 4-19: Electricity need and electricity generation for Atlanta ... 115

Figure 4-20: Electricity need and electricity generation for Houston ... 115

Figure 4-21: Electricity need and electricity generation for Albuquerque ... 116

Figure 4-22: Electricity need and electricity generation for Seattle ... 116

Figure 4-23: Electricity need and electricity generation for San Francisco (100% PV system) ... 117

Figure 4-24: Electricity need and electricity generation for San Francisco (80% PV system) ... 117

Figure 4-25: Electricity need and electricity generation for San Francisco (60% PV system) ... 117

Figure 4-26: Electricity need and electricity generation for Atlanta ... 118

Figure 4-27: Electricity need and electricity generation for Baltimore ... 118

Figure 4-28: Electricity need and electricity generation for Denver ... 118

Figure 4-29: Electricity need and electricity generation for Helena ... 119

Figure 4-30: Electricity need and electricity generation for Los Angeles ... 119

Figure 4-31: Electricity need and electricity generation for Seattle ... 119

Figure 4-32: Electricity need and electricity generation for Albuquerque ... 119

Figure 4-33: Electricity need and electricity generation for Chicago ... 120

Figure 4-34: Electricity need and electricity generation for Duluth ... 120

Figure 4-35: Electricity need and electricity generation for Fairbanks ... 120

Figure 4-36: Electricity need and electricity generation for Houston ... 121

Figure 4-37: Electricity need and electricity generation for Las Vegas ... 121

Figure 4-38: Electricity need and electricity generation for Miami ... 121

Figure 4-39: Electricity need and electricity generation for Minneapolis ... 122

Figure 4-40: Electricity need and electricity generation for Phoenix... 122

Figure 4-41: DOE climate zone classification... 123

x

Figure 4-43: Life-time energy cost savings ... 125

Figure 4-44: Annual energy generation ... 126

Figure 4-45: Lifetime energy generation costs ... 126

Figure 4-46: Life-Cycle cost analysis, green roofs ... 127

Figure 4-47: Life-Cycle Cost Analysis, Cool roofs ... 128

Figure 4-48: Life-Cycle Cost Analysis, PV Systems ... 129

Figure 4-49: Estimated payback periods ... 130

Figure 4-50: Avoided CO2 emissions (kg/lifetime) ... 131

Figure 4-51: Percentage of different roof types as a function of different environmental impacts ... 133

Figure 4-52: Annual energy savings kWh.ft2.yr ... 134

Figure 4-53: Energy cost savings ($/ft2.yr) ... 134

Figure 4-54: Annual energy generation ... 135

Figure 4-55: Energy generation costs ... 135

Figure 4-56: Life-cycle cost analysis of different green roofs ... 136

Figure 4-57: Life-cycle cost analysis of different cool roofs ... 137

Figure 4-58: Life-cycle cost analysis ... 138

Figure 4-59: Estimated payback periods ... 138

Figure 4-60: Avoided CO2 emissions (kg/lifetime) ... 139

Figure 4-61: Percentage of different roof types as a function of different environmental impacts ... 141

Figure 4-62: Annual energy savings (kWh/ft2.yr) ... 142

Figure 4-63: Energy Cost savings ($/ft2.yr)... 142

Figure 4-64: Annual energy generation (kWh/ft2.yr) ... 143

Figure 4-65: Energy generation costs ($/ft2.yr) ... 143

Figure 4-66: Life-cycle cost analysis of different types of green roofs ... 144

Figure 4-67: Life-cycle cost analysis of different types of cool roofs ... 145

Figure 4-68: Life-cycle cost analysis of different types of PV systems ... 146

Figure 4-69: Avoided CO2 emissions ... 146

Figure 4-70: Estimated payback periods ... 147

Figure 4-71: Percentage of different roof types as a function of different environmental impacts ... 149

Figure 4-72: Duke Environment Hall ... 150

Figure 4-73: The Rooftop Garden, Environment Hall [166] ... 151

Figure 4-74: Photovoltaic Rooftop Panels [166]... 152

Figure 4-75: Solar PV cells ... 152

xi

Figure 4-77: Total electricity consumption [165] ... 153

Figure 4-78: Environment Hall model [164] ... 154

Figure 4-79: Electricity produced by photovoltaic system, Environment Hall ... 154

Figure 4-80: Annual building’s electricity consumption, Environment Hall ... 155

Figure 4-81: Annual energy savings, Environment Hall ... 156

Figure 4-82: Energy cost savings for green roof and cool roof, Environment Hall ... 156

Figure 4-83: Annual energy generation, Environment Hall ... 157

Figure4-84: Net cost savings for green roofs, Environment Hall ... 158

Figure4-85: Net cost saving for cool roofs, Environment Hall ... 158

Figure 4-86: Net cost saving for PV systems, Environment Hall ... 159

Figure 4-87: Estimated payback periods, Environment Hall ... 159

Figure 4-88: Percentage of different roof types as a function of different environmental impacts, Environment Hall ... 162

Figure 4-89: The sensitivity of different roof systems based on environmental impacts... 165

Figure 4-90: The sensitivity of different PV systems based on installation costs ... 166

Figure 4-91: The sensitivity of different PV systems, 50% reduction in installation costs ... 166

Figure 4-92: The sensitivity of different PV systems based on considered analysis horizon (40 years) .... 167

xii

LIST OF SYMBOLS

No. Symbol Description

1 𝑿𝒓 𝐏𝒓𝒆𝒔𝒆𝒏𝒕𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒘𝒊𝒕𝐡𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝐞𝒓

2 𝒀𝒔 𝐏𝒓𝒆𝒔𝒆𝒏𝒕𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒘𝒊𝒕𝐡𝒓𝒐𝒐𝐟𝒕𝒚𝒑𝒆𝒔

4 𝒁𝒕 𝐏𝒓𝒆𝒔𝒆𝒏𝒕𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒘𝒊𝒕𝐡𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒕

5 𝑰𝑴𝑿𝒓𝟏

𝐈𝒎𝒑𝒂𝒄𝒕𝒐𝒇𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓𝒐𝒏𝑨𝒏𝒏𝒖𝒂𝒍𝑬𝒏𝒆𝒓𝒈𝒚𝑺𝒂𝒗𝒊𝒏𝒈𝒔(𝑬𝑼𝑰 = 𝐤𝐖𝐡/𝐟𝒕𝟐∗ 𝒚𝒓)

6 𝑰𝑴𝒀𝒔𝟏

𝐈𝒎𝒑𝒂𝒄𝒕𝒐𝒇𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔𝒐𝒏𝑨𝒏𝒏𝒖𝒂𝒍𝑬𝒏𝒆𝒓𝒈𝒚𝑺𝒂𝒗𝒊𝒏𝒈𝒔 (𝑬𝑼𝑰 = 𝐤𝐖𝐡/𝒇𝒕𝟐∗ 𝒚𝒓)

7 𝑰𝑴𝒁𝒕𝟏

𝐈𝒎𝒑𝒂𝒄𝒕𝒐𝒇𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒕𝒚𝒑𝒆𝒕𝒐𝒏𝑨𝒏𝒏𝒖𝒂𝒍𝑬𝒏𝒆𝒓𝒈𝒚𝑺𝒂𝒗𝒊𝒏𝒈𝒔 (𝑬𝑼𝑰 = 𝐤𝐖𝐡/𝒇𝒕𝟐∗ 𝒚𝒓)

8 𝑰𝑴𝑿𝒓𝟐 𝐈𝒎𝒑𝒂𝒄𝒕𝒐𝒇𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓𝒐𝒏𝑬𝒏𝒗𝒊𝒓𝒐𝒏𝒎𝒆𝒏𝒕(𝒌𝒈/𝒇𝒕 𝟐

∗ 𝒚𝒓𝑪𝑶𝟐)

9 𝑰𝑴𝒀𝒔𝟐

𝐈𝒎𝒑𝒂𝒄𝒕𝒐𝒇𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔𝒐𝒏𝑬𝒏𝒗𝒊𝒓𝒐𝒏𝒎𝒆𝒏𝒕 (𝒌𝒈/𝒇𝒕𝟐 ∗ 𝒚𝒓𝑪𝑶𝟐)

10 𝑰𝑴_𝒁𝒕𝟐 𝐈𝒎𝒑𝒂𝒄𝒕𝒐𝒇𝑷𝒀𝒔𝒚𝒔𝒕𝒆𝒎𝒕𝒚𝒑𝒆𝒕𝒐𝒏𝑬𝒏𝒗𝒊𝒓𝒐𝒏𝒎𝒆𝒏𝒕 (𝒌𝒈/𝒇𝒕

𝟐

∗ 𝒚𝒓𝑪𝑶𝟐)

11 𝑪𝑿𝒓 𝐂𝒐𝒔𝒕𝒐𝒇𝒊𝒏𝒔𝒕𝒂𝒍𝒍𝒂𝒕𝒊𝒐𝒏𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓

12 𝑪𝒀𝒔 𝐂𝒐𝒔𝒕𝒐𝒇𝒊𝒏𝒔𝒕𝒂𝒍𝒍𝒂𝒕𝒊𝒐𝒏𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔

13 𝑪𝒁𝒕 𝐂𝒐𝒔𝒕𝒐𝒇𝒊𝒏𝒔𝒕𝒂𝒍𝒍𝒂𝒕𝒊𝒐𝒏𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒕𝒚𝒑𝒆𝒕

14 𝑪′𝑿𝒓 𝐅𝒊𝒙𝒆𝒅𝒊𝒏𝒔𝒕𝒂𝒍𝒍𝒂𝒕𝒊𝒐𝒏𝒄𝒐𝒔𝒕𝒇𝒐𝒓𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓

15 𝑪′𝒀𝒔 𝐅𝒊𝒙𝒆𝒅𝒊𝒏𝒔𝒕𝒂𝒍𝒍𝒂𝒕𝐢𝒐𝒏𝒄𝒐𝒔𝒕𝒇𝒐𝒓𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔

16 𝑪′𝒁𝒕 𝐅𝒊𝒙𝒆𝒅𝒊𝒏𝒔𝒕𝒂𝒍𝒍𝒂𝒕𝒊𝒐𝒏𝒄𝒐𝒔𝒕𝒇𝒐𝒓𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒕𝒚𝒑𝒆𝒕

17 𝑹𝑿𝒓 𝐑𝒆𝒑𝒍𝒂𝒄𝒆𝒎𝒆𝒏𝒕 𝑪𝒐𝒔𝒕𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓

18 𝑹𝒀𝒔 𝐑𝒆𝒑𝒍𝒂𝒄𝒆𝒎𝒆𝒏𝒕 𝑪𝒐𝒔𝒕𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔

19 𝑹𝒁𝒕 𝐑𝒆𝒑𝒍𝒂𝒄𝒆𝒎𝒆𝒏𝒕 𝑪𝒐𝒔𝒕𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒕𝒚𝒑𝒆𝒕

20 𝑪𝑴𝑿𝒓 𝐌𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆𝒄𝒐𝒔𝒕𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝐠𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓

21 𝑪𝑴𝒀𝒔 𝐌𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆𝒄𝒐𝒔𝒕𝒐𝒇𝒐𝒏𝒆𝒖𝒏𝒊𝒕𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝐟𝒕𝒚𝒑𝒆𝒔

xiii

No. Symbol Description

23 𝑪𝑴′𝑿𝒓 𝐅𝒊𝒙𝒆𝒅𝒎𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆𝒄𝒐𝒔𝒕𝒇𝒐𝒓𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓

24 𝑪𝑴′𝒀𝒔 𝐅𝒊𝒙𝒆𝒅𝒎𝒂𝒊𝒏𝒕𝐞𝒏𝒂𝒏𝒄𝒆𝒄𝒐𝒔𝒕𝒇𝒐𝒓𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔

25 𝑪𝑴′𝒁𝒕 𝐅𝒊𝐱𝒆𝒅𝒎𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆𝒄𝒐𝒔𝒕𝒇𝒐𝒓𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒕𝒚𝒑𝒆𝒕

26 L 𝐭𝒐𝒕𝒂𝒍𝒔𝒖𝒓𝒇𝒂𝒄𝒆𝒂𝒓𝒆𝒂𝒐𝒇𝒓𝒐𝒐𝒇

27 𝑪𝒌𝑾𝐡 $𝒇𝒐𝒓𝟏𝒌𝑾𝐡

28 𝑰𝒓 𝐈𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔𝒇𝒐𝒓𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒔

29 𝑰𝒔 𝐈𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔𝒇𝒐𝒓𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒔

30 𝑰𝒕 𝐈𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝒗𝒂𝒓𝒊𝒂𝒃𝒍𝒆𝒔𝒇𝒐𝒓𝑷𝑽𝒔𝒚𝒔𝒕𝒆𝒎𝒔

31 𝒈𝒙 𝐃𝒆𝒈𝒓𝒂𝒅𝒂𝒕𝒊𝒐𝒏 𝒓𝒂𝒕𝒆 𝒐𝒇 𝒈𝒓𝒆𝒆𝒏 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

32 𝒈𝒚 𝐃𝒆𝒈𝒓𝒂𝒅𝒂𝒕𝒊𝒐𝒏 𝒓𝒂𝒕𝒆 𝒐𝒇 𝒄𝒐𝒐𝒍 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

33 𝒈𝒛 𝐃𝒆𝒈𝒓𝒂𝒅𝒂𝒕𝒊𝒐𝒏 𝒓𝒂𝒕𝒆 𝒐𝒇 𝑷𝑽 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

34 𝒏𝒙𝒓 𝐋𝒊𝒇𝒆𝒕𝒊𝒎𝒆 𝒐𝒇 𝒈𝒓𝒆𝒆𝒏 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

35 𝒏𝒚𝒔 𝐋𝒊𝒇𝒆𝒕𝒊𝒎𝒆 𝒐𝒇 𝒄𝒐𝒐𝒍 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

36 𝒏𝒛𝒕 𝐋𝒊𝒇𝒆𝒕𝒊𝒎𝒆 𝒐𝒇 𝑷𝑽 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

37 𝒏′𝒙𝒓 𝐌𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒑𝒆𝒓𝒊𝒐𝒅𝒔 𝒐𝒇 𝒈𝒓𝒆𝒆𝒏 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

38 𝒏′𝒚𝒔 𝐌𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒑𝒆𝒓𝒊𝒐𝒅𝒔 𝒐𝒇 𝒄𝒐𝒐𝒍 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

39 𝒏′𝒛𝒕 𝐌𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒑𝒆𝒓𝒊𝒐𝒅𝒔 𝒐𝒇 𝑷𝑽 𝒔𝒚𝐬𝒕𝒆𝒎𝒔

40 𝐢 𝐑𝒆𝒂𝒍 𝒊𝒏𝒕𝒆𝒓𝒆𝒔𝒕 𝒓𝒂𝒕𝒆

41 𝐢′ 𝐂𝒐𝒎𝒃𝒊𝒏𝒆𝒅 𝒊𝒏𝒕𝒆𝒓𝒆𝒔𝒕 𝒓𝒂𝒕𝒆 𝒇𝒐𝒓 𝐩𝒆𝒓𝒊𝒐𝒅 𝒐𝒇 𝒎𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆

42 𝒏′′𝒙𝒓 𝐅𝒊𝒙𝒆𝒅 𝑴𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒑𝒆𝒓𝒊𝒐𝒅𝒔 𝒐𝒇 𝒈𝒓𝒆𝒆𝒏 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝐦𝒔

43 𝒏′′𝒚𝒔 𝐅𝒊𝒙𝒆𝒅 𝑴𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒑𝒆𝒓𝒊𝒐𝒅𝒔 𝒐𝒇 𝒄𝒐𝒐𝒍 𝒓𝒐𝒐𝒇 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

44 𝒏′′𝒛𝒕 𝐅𝒊𝒙𝒆𝒅 𝑴𝒂𝒊𝒏𝒕𝒆𝒏𝒂𝒏𝒄𝒆 𝒑𝒆𝒓𝒊𝒐𝒅𝒔 𝒐𝒇 𝑷𝑽 𝒔𝒚𝒔𝒕𝒆𝒎𝒔

45 𝒌𝑿𝒓 𝐍𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑹𝒆𝒑𝒍𝒂𝒄𝒆𝒎𝒆𝒏𝒕𝒐𝒇𝒈𝒓𝒆𝒆𝒏𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒓

46 𝒌𝒀𝒔 𝐍𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑹𝒆𝒑𝒍𝒂𝒄𝒆𝒎𝒆𝒏𝒕𝒐𝒇𝒄𝒐𝒐𝒍𝒓𝒐𝒐𝒇𝒕𝒚𝒑𝒆𝒔

1

Chapter 1

INTRODUCTION

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O

Primarily architects and designers make major design decisions regarding a building’s

sustainability during the early design stages. Improving thermal performance of building

envelops is widely considered in sustainable design from different points of view, i.e.

architecture, structure, and construction [1, 2]. Changing building design parameters such as

form, orientation, and envelope configurations leads to developing sustainable buildings.

The roof, as primary part of the envelope, is also an important area in the conceptual stage of

building design. Roof surfaces are designed and utilized as an extra space that can improve the

energy performance and economic aspects of a building [3]. The roof surface also offers a weatherproof barrier for the building’s interior [4]. Furthermore, the roof system is one of the

building’s elements requiring frequent replacement. Thus, changing the roof’s characteristics

and materials is relatively easier than other envelopes. The average lifetime of a conventional

roof is 15 to 20 years before it needs replacement [5-7].

This therefore gives rise to utilizing roof spaces in terms of different roof functions and

technologies for sustainable development [5]. These roof technologies are considered an

efficient measurement or strategy that serves as a benchmark for designers to improve the

performance of a building with regard to energy and environment.

Among different roof types’ technologies and functions, there are debates and challenges

2

financial aspect, energy performance, and environmental recognition, all of which are

considered in sustainable development [4].

In addition, integrating different roof functions in a contingent way can improve the efficiency

of the system and avoid single roof type deficiencies. However, this requires creating a

quantitative connection between the spectrums of roof function performance factors [8].

Therefore, this research develops a suitable methodology to consider these factors when

selecting integral optimal options for different roof functions. It primarily investigates the

combination of different roof functions in satisfying different needs, such as environmental

impact and energy consumption, while minimizing the project’s financial aspect.

R

Q

This research attempts to address following question:

What are the best and/or optimal strategies of utilizing roof areas in commercial buildings to

achieve sustainability goals such as Net Zero Energy Buildings (NZEBs) or Carbon-Neutral

buildings?

In order to achieve this purpose, the followings need to be addressed:

1) Which parameters should be considered in sustainable roof design?

2) Of those parameters considered, which ones should be assumed to have common and

typical values, and which ones should be directly estimated, such as energy

3

3) How do Energy Efficiency & Renewable Energy (EERE) technologies like cool roofs,

green roofs, and PV systems impact building energy consumption and energy

production?

4) What are the environmental impacts of these technologies?

5) What are the relationships between EERE technologies in terms of installation,

operation cost, and environmental impact?

6) How can the energy, cost, and environmental impact of these strategies be assessed or

evaluated?

7) Is there any integration among different roof functions? If yes, should those be

considered in this research?

8) Can a framework provide a universal model for use in other climates and locations?

9) Ultimately, can a combined roof be evaluated for whether it can perform more

satisfactory compared to traditional ones with regard to sustainable development?

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G

The main goal of this research is to develop a framework that includes an optimization

mathematical model as a core that is fed by required data from energy, cost, and environmental

analyses in order to present the optimal combination of different roof functions. The core

optimization model attempts to maximize the cost savings associated with energy performance,

roof type installation costs, maintenance, and operational costs, while considering the

environmental impact of the overall combination of roof systems.

4

O

R

This study addresses the gap in existing roof technology research by applying energy and cost

analysis and environmental assessment. The research seeks to meet the following objectives:

1) Develop an optimization mathematical model, which can help designers optimize

technology implementation cost.

2) Evaluate energy characteristics for different roof functions using simulation modeling

technology.

3) Evaluate environmental impacts of roof function alternatives using simulation,

literature review, and existing data.

4) Develop a Cost Benefit Analysis (CBA) for different roof functions considering their

NPV value.

5) Develop an Excel/VBA tool or MATLAB to validate the proposed optimization model.

6) Develop a database to estimate the behavior of combining different roof types in

planning and preliminarily level analyses.

The dissertation is organized as follows: Following this introductory section, a review of the

studies and principles of different roof functions as found in the literature is presented,

followed by a summary and conclusion. Next is methodology section that details the proposed

mathematical framework and its components. Results of the evaluation of proposed methods

are highlighted next, followed by a conclusion section and recommendations for additional

5

Chapter 2

LITERATURE REVIEW

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A

Integrated Roof Systems

Roof surfaces play significant roles in sustainable development on both the building and urban

scale. Roof designers have applied different roof technologies in order to decrease storm water

runoff, build energy consumption, create Urban Heat Island (UHI), and produce electricity.

Most roof designs are limited to a single technology or specific strategy, yet coupling various

roof systems can provide active and passive benefits in both energy and cost. [9, 10].

The integrated roof systems concepts incorporate multiple aspects and functions, such as green

roofs, cool roofs, various materials, daylighting, PV systems, and water harvesting systems.

The integration of roof systems is an ongoing discussion. There are several installed integrating

roof systems in the world; however, a comprehensive assessment of benefits and costs resulting

from the coupled configurations is still lacking. [10-14].

D

This section presents the definition of some terms used in the literature and content.

Energy Performance

The energy performance of a building is the energy-related characteristics of the building,

including the amount of energy consumption, energy savings potential, and energy generation.

6 Energy Performance Metrics

Energy Efficiency

The key concept of energy efficiency is to generate the same amount of output using less

energy. It has financial benefits for both customers and manufacturers. Bosseboeuf et al. have

two definitions for energy efficiency:

- Economic efficiency: more output or improved standards of living with the same or

less amount of energy.

- Techno-economic efficiency: reducing particular energy due to technical progress,

changes in behavior, better management, and so forth [16, 17].

Equation 2-1 shows the quantitative measure of energy efficiency based on selected energy

input and useful output [17]:

𝑈𝑠𝑒𝑓𝑢𝑙 𝑜𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑐𝑒𝑠𝑠 𝐸𝑛𝑒𝑟𝑔𝑦 𝐼𝑛𝑝𝑢𝑡 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑐𝑒𝑠𝑠

2-1

Energy Generation

Applying renewable energy to a building produces energy that can support the building’s

energy demands in terms of electricity, heating, and cooling. In 2015, U.S. power plants

utilized energy sources including water, wind, biomass, wood and waste, geothermal, and solar

to produce around 13% of the electricity consumed in the United States [18, 19]

Energy Quantity

7

Energy Use Intensity (EUI) represents the amount of energy used in a building. EUI is stated

as energy per year per square foot and is often given as [kBtu/ft2/yr]. It is estimated by dividing

the total annual energy usage of the building by the total gross floor area of the building, such

as in Equation 2-2. In general, a low EUI indicates good energy performance [20, 21].

𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑠𝑒 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 (𝐸𝑈𝐼) = 𝑇𝑜𝑡𝑎𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑠𝑒 𝐺𝑟𝑜𝑠𝑠 𝐹𝑙𝑜𝑜𝑟 𝐴𝑟𝑒𝑎

2-2

Energy Peak Demand

For industrial and commercial sectors, electricity consumption is based on two parameters:

actual energy usage (kWh) and penalty fee, which is based on the highest level of demand

(peak) during a specific period (week, month, or year). This charge is based on a price per kW,

typically the peak kW of the billing period. Energy peak demand is one of the most important

variables that should be considered for estimating the amount of additional capacity required

to ensure an adequate supply of energy and the size of equipment [22].

Thermal Comfort

According to a definition produced by the American Society of Heating, Refrigerating and Air conditioning Engineers (ASHRAE), thermal comfort is “the condition of the mind in which

satisfaction is expressed with the thermal environment” [23]. Thermal comfort depends on

numerous parameters. Two main parameters are “personal factors” like occupant activity,

clothing level, age, and sex, and “environmental factors” including dry-bulb temperature and

mean radiant temperature, which refers to surrounding objects’ temperature, humidity, and air

8

Building Simulation programs

Building simulation programs are utilized to analyze buildings’ energy performance and

thermal comfort during the day, year, or life-cycle. There are various available tools that vary

in several aspects, such as their thermodynamic models, graphical user interface, and

application purpose. In 2010, the U.S. Department of Energy (DOE) Building Energy Software

Tools Directory (BESTD) website published a list of Building Performance Simulation (BPS)

programs, which consists of around 389 tools. Therefore, in order to facilitate the selection

process, it is critical to understand the limitations and capabilities of the desired tools and identify users’ needs [25, 26].

This section introduces a selection of energy simulation engines; the conclusion will compare

these programs.

Quite a bit of research has been conducted on the criteria and requirements of BPS tools. For

example, Crawly et al. compared the capabilities and features of 20 main building energy

simulation tools such as BLAST, BSim, DOE-2.1E, ECOTECT, EnergyPlus, EQUEST, and

HEED based on 18 categories including reporting, validation, user interface, and links to other

programs [27]. Attia and De Herde compared 10 Building Performance Simulation (BPS) tools

including HEED, e-Quest, ENERGY-10, DesignBuilder, ECOTECT, Vasari, Solar Shoebox,

Open Studio Plug-in, IES-VE-Ware and BEopt [28]. Lam et al. studied five tools based on four

main criteria: usability, functionality, reliability, and prevalence [26, 29].

From the review of previous studies, five major criteria can be considered for BPS tool

9

1- Usability and information management

2- Integration of intelligent design knowledge-base

3- Accuracy

4- Interoperability

5- Integration with design process [26].

This study used the EnergyPlus program [30] to evaluate the impact of multiple roof parameters on a building’s energy performance. The EnergyPlus simulation program was

selected due to its high accuracy level, wide application ranges, and other features as described

below.

EnergyPlus program

EnergyPlus is a new generation of simulation tool support by the U.S. Department of Energy.

This program combines the main features and capabilities of both BLAST and DOE-2 and

includes new features. EnergyPlus is a modular, structured code that is easy to maintain,

update, and extend. All input and output data files are in sample format, which can be utilized

by other programs and databases. EnergyPlus is a simulation engine with a variable time step

to analyze the heating system, cooling system, and plant and electrical system. Integrated

simulation offers a more accurate calculation of space temperature, building loads, heat gain,

and other energy flows [24, 27, 31].

Building Geometry and Climate Data

In order to perform the energy analysis in EnergyPlus, two input files are needed: a building

10

weather data file, which in our case is the Typical Meteorological Year (TMY), and default

parameters for each specific roof [32].

To examine the energy performance of various roof systems, the prototype model was

developed using Google SketchUp7 program with the OpenStudio program plugged into it [33,

34].

Open Studio is an open source analysis platform that leverages EnergyPlus and Radiance

simulation engines. It was developed by the National Renewable Energy Laboratory (NREL)

and facilitates an integrated whole-building energy analysis. A plug-in to Google SketchUp

allows users to build geometry with other required EnergyPlus input data [24, 35].

Spectral Properties of Solar Radiation

The properties of solar spectra need to be understood in order to study the effect of solar

radiation on roof surfaces with different functions. When sunlight arrives on the earth, three

distinct spectrums of solar radiations are identified as illustrated in Figure 2-1. They range

11 Figure 2-1: Atmospheric solar spectra [36]

Figure 2-1 presents the solar distributions based on a pre-wavelength unit basis. As shown,

52% of the solar spectrum arrives in the visible region, 43% is in the near-infrared region, and

a small portion (5% ) is ultraviolet [36, 37].

When solar energy arrives on any surface, it may result in various outcomes. It may be

transmitted, reflected, or absorbed by the surface. In most cases, these three options occur to a

larger or less extent.

The solar radiation spectral is affected by the radiated materials’ type and thickness. How the

materials transmit, reflect, and absorb the solar radiation vary for different roof functions.

Solar Reflectance

Solar reflectance is defined as the ratio of the reflected solar radiation to the incoming solar

radiation, which arrives on the surface from all surrounding directions. It covers wavelengths

12 Thermal Emittance of Solar Radiation

Thermal emittance is the ability of a surface to release the absorbed heat. It measures how well

a surface emits energy compared to a blackbody surface at the same temperature [38, 39].

Absorbance

Solar absorbance is the portion of arrived solar radiation that is absorbed by the surface of

materials. Some amount of solar radiation that arrives to the surface is absorbed and increases the materials’ temperature.

If the solar reflectance (or reflectivity) data is available, then absorbance is equal to 1.0 minus

reflectance (for opaque materials). The values for solar reflectance range from 0.0 to 1.0.[38,

39].

Grid-connected PV systems

A grid connected system provides the ability for the PV system to transfer extra electricity

demands to the power line. When the electricity demands are lower than PV output, the excess

electricity can be sold back into the grid-connected system. Figure 2-2 demonstrates the

13 Figure 2-2: Grid-connected photovoltaic system [40]

Cost Analysis

Net Present Value Analysis

The net present value (NPV) analysis prices future costs in terms of current dollars. It allows

for a comparison of the total costs over a specific time period [42] .

Interest Rate

The nominal interest rate per year (r) is the annual interest rate without considering the effect

of compounding, such as inflation rate. When other periods of time are used, it is called

effective interest rate.

Inflation Rate

Inflation rate is an occurrence that causes purchasing power reduction, and as result, revenue

and cost escalation. Inflation rate influences the value of future cash flow and should be

14 Real Interest Rate

The real interest rate is an interest rate that has been adjusted considering inflation rate effects.

The concept of real interest rate is useful when accounting for the impact of inflation in order

to achieve an accurate estimation of real cost. The difference between real interest rate and

nominal interest rate is summed up in Equation 2-3[44, 45].

𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑟𝑎𝑡𝑒 – 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 = 𝑅𝑒𝑎𝑙 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑟𝑎𝑡𝑒 2-3

Degradation Rate

Decreasing the power output over time due to the decreasing efficiency of the system is known

as degradation rate. Degradation rate is an important economic factor because it is directly

related to power generation and occurs as result of future cash flows [46].

Environmental Performance

Environmental Impact Assessment

The Environmental Impact Assessment (EIA) definition adopted by the International Association for Impact Assessment (IAIA, 2009) is “the process of identifying, predicting,

evaluating, and mitigating the biophysical, social and other relevant effects of proposed development proposals prior to major decisions being taken and commitments made” [47].

All types of energy have some harmful effect on the environment; however, the effect of

renewable energy is less than with other sources. Understanding the environmental effects

associated with different types and sources of energy is critical for sustainable development

15

Greenhouse gas emission

While the entire world is tackling global warming concerns, greenhouse gas (GHG) reduction

remains one of the most interesting parameters for environmental protection. More than 80%

of U.S. greenhouse gas emissions is associated with energy-related carbon dioxide. Carbon

dioxide emissions in the commercial sector are mostly related to energy consumption for

lighting, heating, and cooling. Using green technologies such as green roofs and cool roofs can reduce a building’s energy demands and reduce the amount of GHG emissions over their

lifecycle [49]. GHG emissions are also related to the energy source. For example, coal

combustion generates more greenhouse gas than natural gas or petroleum [39].

Roof system technologies

Applying various roof technologies such as cool roofs, several materials, daylighting, PV

systems, and water harvesting is an effective measurement for sustainable roof design. Roof

system technologies have been applied for various purposes including storm water

management, energy consumption reduction, urban heat island mitigation, and electricity

generation. In this study, three functions of roofing systems are selected based on the literature,

including green roofs, cool roofs, and Photovoltaic systems. The different types of systems,

materials, and their performance are explained below.

Cool Roofs

A cool roof is a roof that stays cool in the sun due to low solar absorption and high thermal

16

sun is the one that determines whether a roof is cool or not. The lower temperature of cool

roofs leads to reduced heat transfer into interior spaces and a resulting reduction in the cooling

load of the building. According to the U.S. Green Building Council, a roof is considered cool

if it has a solar reflectance value of at least 0.7 and thermal emittance value of at least 0.9 on a

scale of 0 to 1 [38, 50, 51].

Cool roof material:

With the recent development of new materials and techniques presenting advanced thermal

characteristics including high solar reflectance and infrared emittance, new cooler materials

are being generated. These are categorized in four phases including:  Highly reflective and emissive light-colored materials

 Cool-colored materials

 Phase change materials

 Dynamic cool materials [52]

Cool Roof Coating:

Roofs can receive a solar reflective coating that helps solar radiation reflections. Coatings are

thick paints containing reflective pigments that can reflect sunlight and keep roof surfaces from

chemical damage and ultra-violet (UV) incidence.

There are two types of coating:

 White coating: White materials are a popular option for building surfaces that cannot

17

 Color coating: Cool-colored roofs are designed to increase sun reflection while

maintaining the color and aesthetic of traditional nonwhite roofing products [53-56].

Roof mist cooling systems or evaporative cooling systems:

A roof misting system lowers surface temperatures by spraying an extremely small amount of

water across the roof, allowing the water to cool the roof as it evaporates [37, 53, 56].

The benefits of roof mist systems include reducing surface temperature, air-conditioning usage,

and cooling energy consumption, as well as improving air conditioning efficiency. However,

it is not sustainable in areas where water is valuable and scarce [57, 58].

Roofs that are covered by PV panels, vegetation, stone ballast or gravel (ballasted roof) can

reflect solar radiation and act like a cool roof. The different types of roof systems, along with

the methods of making them cool, are presented below:

1) Single-ply Membranes:

Single-ply membranes consist of pre-fabricated sheets of rubber polymers that are attached to

the roof with mechanical fasteners or chemical adhesives. They can also be held in place using

ballast such as gravel or stones. Some membranes are typically white and reflect sunlight, such

as TPO (thermoplastic polyolefin) and PVC (polyvinyl chloride). Other membranes are

typically black and should be formulated in the factory or coated to make them cool, such as

EPDM (ethylene propylene diene M-class) [54, 59] .

2) Built-Up Roofs:

Built up roofs consist of a base sheet, fabric reinforcement layers, and often a dark protective

18

can make the surface layer cool. Other options for cooling surfaces are embedding mineral

granules or factory-applied coating. Cool coating can be applied directly on top of the typical

dark roof.

3) Modified Bitumen Sheet Membranes:

These types of roof systems consist of one or more layers of plastic or rubber material using

reinforcing fabrics. The surface layer is covered with mineral granules or a smooth finish. The

modified bitumen sheet can be pre-coated at the factory.

4) Spray Polyurethane Foam roofs:

These roof systems are composed of two combined liquid chemicals. These materials react and

create one solid piece attached to the roof. Protective coatings are used to keep the foam surface

away from mechanical, moisture, and UV damage. These coatings can be reflective and can

cool the surface.

5) Shingled Roofs:

Shingled roofs contain overlapping panels. These panels consist of different materials such as

fiberglass asphalt, polymers, or metals. Asphalt shingles can be coated with granules for

protection and better solar reflectance. Other roof shingles materials including wood,

polymers, or metals can be coated at the factory or in the field. Metal shingles are explained in

19 6) Tile Roofs:

Tile roofs can be composed of clay, slate, or concrete. They vary in color and thermal

characteristics based on the earth’s composition. Some have good reflection amounts, while

others need to be glazed or coated to change from none-cool tiles into cool roof tiles.

7) Metal Roofs:

Metal roofs commonly have high solar reflectance, but low thermal emittance. Those that have

enough SRI (Solar Reflectance Index) are considered a cool roof. Painting metal roofs at the

factory or in the field can enhance their solar reflectance and thermal emittance [51, 53, 54,

56, 59].

Cool roof performance

A roof can be considered cool when it meets two requirements. First, it must have both the

minimum amount of solar reflectance and thermal emittance, as shown in Table 2-1. Second,

it must have the minimum Solar Reflectance Index (SRI), which permits roofs to meet the

minimum thermal emittance and solar reflectance requirement [54].

Table 2-1: Typical minimum cool roof requirement, California energy commission

Roof Type

Solar Reflectance [3-year aged]

AND

Thermal Emittance [New or aged]

OR

Solar Reflectance Index (SRI) [3-year aged]

Low Sloped 0.55 0.75 64

Steep Sloped 0.20 0.75 16

Cool roof materials in any color reflect solar radiations in the near infrared region, which

20 Figure 2-3: a) Spectral solar power distribution, (b) Solar spectral reflectance of cool and standard

brown surfaces [51]

Figure 2-3 demonstrates the spectral reflectance of a brown surface and cool color material.

They have similar reflectance value in the visible range of the solar spectrum, but the cool

material has higher reflectance in the near infrared range, which mainly causes thermal [51,

52]. Cool materials also emit radiation more efficiently than regular roofs due to higher thermal

emittance. Thus, the roof surface remains cool and reduces the building’s heat gain within the

envelope, which results in a cooling load reduction [14, 51]. Equation 2-4 demonstrates the

thermal balance of a horizontal surface under the sun:

(1 − 𝑅)𝐼 = ℇơ (𝑇_𝑠4_{− 𝑇}

𝑠𝑘𝑦4 ) + ℎ𝑐(𝑇𝑠− 𝑇𝑎) − ƛ

𝑑𝑇 𝑑𝑥

2-4

Where:

I is the insulation (W/m2),

21

ℎ𝑐is the convention coefficient (W/m2 K),

𝑇_𝑠𝑘𝑦 is the sky temperature (K),

𝑇_𝑎is the air temperature (k),

R is solar reflectance or albedo of the surface,

ℇ is the emissivity of the surface,

ƛ is the thermal conductivity of the surface (W/mk), and

𝑑𝑇

𝑑𝑥 is the temperature gradient( in the x axis) [60].

If the roof has enough insulation, then solar reflectance and infrared emittance are two major

parameters that impact the roof’s thermal performance. Solar reflectance is a more significant

factor affecting the surface temperature during the day, while emissivity has less impact. On

the contrary, thermal emittance becomes the main effective factor during the night due to the

thermal performance of the roof compared to solar reflectance [60]. Note that the thermal

emittance can be reduced due to high humidity, cloudiness, or thick fog.

Replacing cool roofing material with a typical dark roof reduces cooling demands and peak

demands due to reduced heat transfer from the cooler roof surface to indoor space. There are

numerous studies about cooling energy savings using cool roof surfaces. The amount of energy

22

features, HVAC systems, and location. The effectiveness of cool roof materials is more

considerable in older buildings with little or no insulation [37, 51, 52, 61].

Using cool roof materials can also enhance the roof’s lifetime due to less daily fluctuations in

surface temperature. In large scale application, cool roof materials can mitigate the ambient air

temperature and reduce heat island effect in urban scale. As a result, the advantages of cool

roof design can be accounted for buildings, city and global scale [37, 52].

Green roofs

A green roof, also known as a vegetative roof can be defined as “an assembly of interacting

components designed to waterproof and normally insulate a building’s top surface that

includes, by design, vegetation and related landscaping elements” [62]. A green roof consists

of a soil layer (growing media), vegetation layer, and typical construction materials including

a drainage layer, roof barrier, and waterproof membrane [63]. Green roofs generally are

categorized into three different types depending on use, construction, and methods used to

carry out the work.

- Intensive greening: Intensive greening roofs consist of all sizes and types of plants including

shrubs, coppices, grassed areas, even an occasional tree. These may be planted on the same

level, at different heights, or in separate plantings around the site. They are mostly used for

commercial buildings with large green spaces.

- Simple intensive greening: Simple intensive greening roofs consist of different plants like

23

is not as wide as intensive greening. They generally need little watering and feeding, and as a

result cost less than intensive greening.

- Extensive greening: Extensive greening roofs are more commonly used for residential and

commercial buildings. Extensive green roofs comprise vegetation cultivation, which requires

little maintenance and less costs compare to intensive and simple intensive greening [64, 65].

According to the FLL Guideline for the Planning, Execution, and Upkeep of Green Roof Sites

(2002), green roofs are categorized into different types ranging from ultra-extensive to

intensive based on plant type and vegetation depth as shown in Table 2-2 [65].

Table 2-2: Standard course depths for different types of roof-greening [65]

Depth of the vegetation support

course in cm 4 6 8 10 12 15 18 20 25 30 35 40 45 50 60 70 80 90 100 125 150 200

Moss-sedum

Sedum-moss- herbaceous plants

Sedum-herbaceous-grass plants

Grass- herbaceous plants

Grass- herbaceous plants

Wild shrubs, coppices

Coppices and shrubs

Coppices

Lawn

Low- lying shrubs and coppices

Medium-height shrubs and coppices

Tall shrubs and coppices

Large bushes and small trees

Medium- sizes trees

24

The Green Roof Calculator tool uses a matrix of green roofs based on Leaf Area Index (LAI)

and soil depth, which are the most important parts in green roof effectiveness [66].

The leaf area index (LAI) is a measure of canopy density and differs based on the types of

vegetation. It has an important impact on transpiration and radiative shading and as result

thermal performance of green roof. The range of LAI is between 1.0 for grasses to more than

10 for bushes and trees. Soil depth is another important parameter for green roof design. Depth

of the soil helps to keep moisture and increase thermal resistance to heat flow. Therefore, LAI

and soil depth are effective parameters for estimating the performance of green roof. Three

alternatives of LAI (0.5, 2.0, and 5.0) and three alternatives of growing media depth (5, 15,

and 30 cm) are considered for different types of green roof (Figure 2-3) [67].

Table 2-3: Different types of green roof based on LAI and soil depth [66]

Number (Type) LAI Soil Depth (cm)

1 5 30

2 2 30

3 0.5 30

4 5 15

5 2 15

6 0.5 15

7 5 5

8 2 5

9 0.5 5

We use these nine green roof types in our calculations for different climate zones in order to

25

5cm in out of range number for soil thickness in EnergyPlus, we changed the soil depth of

green roof types of 7, 8, and 9 to 6cm.

Green roofs performance

Similar to cool roof functions, green roofs also have some reflectance and emissivity. Various

types of vegetation have different reflectivity ranges from 0.15 to 0.5. Foliage also has a high

emissivity range from 0.8 to 0.85. These values can change based on the intensity and leaf area

index (LAI). However, in green roofs, convection and evaporation play an important role for

determining roof surface temperature. The growing media and plants with evaporative cooling

and thermal mass characteristics reduce the roof surface temperature and the temperature of

the area below the roof.

When solar radiation arrives on the green layers, the visible range of radiation is absorbed,

while the near infrared radiation that causes heating is reflected. Evapotranspiration is also an

important factor for heat reduction. Some energy is converted to latent heat or evaporative

cooling through both evaporation from the soil surface and transpiration from the plants. Any

solar energy that is not reflected or removed by evaporation or transpiration is transferred into

growing media by conduction. Energy that is not taken away from surface is conducted into

the growing media and then partly absorbed by the growing media and deposited in the soil.

26 Figure 2-4: Temperature distribution of the green roof at a given diurnal time [68]

In order to understand the heat flux through the vegetation roof, the green roof can be

considered a single vegetation layer on a soil surface. The foliage of the vegetation layer has

specific characteristics such as emissivity, albedo, height, and foliage fractional coverage,

which can affect the heat exchange between the soil layer and atmosphere. The soil layer also

can be considered a homogeneous layer, as sensible latent heat passes through it. The following

phenomena should be accounted for when considering thermal performance of a green roof:

 Long wave and short wave radiative interchange through plants. In addition, there are

multiple reflections between plants and the soil layer.  Convective heat transfer, which is affected by foliage layer.

 Evapotranspiration from the soil and vegetation.

27

The most important benefits of the green roof which are related to solar radiation are the

reduction of energy demand and expansion of the roofing membrane’s lifetime. A green roof

can protect the roofing membrane against ultra-violet (UV) radiation and prevent physical

damage due to the degeneration of insulation and roofing materials. It can block solar radiation

and decrease daily temperature fluctuations and end thermal ranges during summer and winter.

All of these benefits extend the roof’s lifetime [72].

The heat-gain and heat-loss through a green roof is mainly defined by solar radiation. A green

roof is useful for reflecting solar radiation and reducing heat absorption by evapotranspiration.

In addition, it can minimize the amount of heat transmitted through the roof membrane due to

its extra insulation. Determination of green roof materials, foliage characteristic, and soil

features affecting the roof’s thermal performance is necessary for designers [73].

In order to compute the heat transfer through a green roof, two energy balance equations need

to be solved for the foliage layer (ɸf) and soil surface (ɸg). The energy balance at the foliage

level (Ff) is demonstrated in Equation 2-5.

𝐹𝑓 = ơ𝑓 [(𝐼𝑠(1 − 𝛼𝑓) + ℇ𝑓𝐼𝑎𝑖𝑟− ℇ𝑓ơ𝑇𝑓4) +

ơ𝑓ℇ𝑔ℇ𝑓ơ

ℇ₁ (𝑇𝑔

4_{− 𝑇}

𝑓4) + 𝐻𝑓+ 𝐿𝑓

2-5

Where:

ơ𝑓 is the foliage fraction coverage,

𝐼_𝑠 is the total solar irradiance,

𝛼_𝑓 is the shortwave albedo of the foliage layer,

28

ơ is the Stefan-Boltzmann constant (56,685* 10 -8 W/m2 K4),

𝑇_𝑓is the foliage surface temperature,

ℇ_𝑔is the ground emissivity,

ℇ₁is a function both of the ground and the foliage emissivity,

𝐻_𝑓 is the sensible heat flux and,

𝐿𝑓is the latent heat flux.

(Hf) is considered the convective heat exchange between the foliage and the nearby air, which

is sensible heat flux, and (Lf) is considered the heat exchange due to the evaporation at the foliage level. The energy balance equation at the soil level (Fg) is presented in Equation 2-6.

_𝐹

𝑔 = (1 − ơ𝑓 )[𝐼𝑠(1 − 𝛼𝑔) + ℇ𝑔 𝐼𝑎𝑖𝑟 − ℇ𝑔 𝑇𝑔4]

+ơ𝑓ℇ𝑔ℇ𝑓ơ ℇ₁ (𝑇𝑔

4_{− 𝑇}

𝑓4) + 𝐻𝑔+ 𝐿𝑔+ 𝐾 ∗

Ə𝑇_𝑔 Ə𝑧

2-6

Where:

𝛼_𝑔 is the shortwave albedo of the ground,

𝐼𝑎𝑖𝑟 is the total infrared irradiance,

𝐻_𝑔 is the sensible heat flux,

𝐿_𝑔 is the latent heat flux,

K is the ground conductivity,

29 Figure 2-5: The Energy Balance for a Green Roof [39]

Based on the previous equations, the properties of the soil and vegetation layers influence the

thermal behavior of green roofs (Figure 2-5).

A green roof’s equivalent periodic thermal transmittance can be calculated considering the

equivalent outside temperatures (Θe,eq) and the heat flux (ɸ), as in Equation 2-7.

𝑌𝑖𝑒 =

(𝛷_{𝑐𝑜𝑛𝑑,𝑠𝑖,𝑚𝑎𝑥} −𝛷_{𝑐𝑜𝑛𝑑,𝑠𝑖,𝑚𝑖𝑛} ) _𝑠𝑖𝑑𝑦𝑛,𝐶𝑇𝐹 (𝜃𝑒,𝑒𝑞,𝑚𝑎𝑥 −𝜃𝑒,𝑒𝑞,𝑚𝑖𝑛 )

2-7

This equation takes into account the thermal inertia of green roof components, density and

specific heat of the soil, and outdoor temperature, which have more impact on the thermal

behavior of the green roof. According to research on the thermal behavior of green roofs, the

main parameters of components significantly affecting the Yie value are the LAI value and soil

thickness. In a roof with little or no insulation, the effectiveness of the design parameters are

30

The design parameters of software implementation in calculating green roofs’ heat flux are

presented below:

 Height of plants: Limited to the value of 0.01m <height<1.0m.

 Leaf area index: Leaf area per unit area of soil surface, ranging from 0.001<LAI<5.0.

 Leaf reflectivity: Ratio of incident solar radiation reflected, ranging from 0 to 1.

 Leas emissivity: Ratio of thermal radiation emitted from leaf surface compared to black

body at the same temperature, ranging from 0 to 1.

 Minimum stomatal resistance: Plant’s resistance to transport moisture in units of s/m,

normally ranging from 50.0 to 300.0 s/m.

 Roughness: Related to the roughness of a particular material layer. Possible options for

this characteristic are “Very Rough,” “Rough,” “Medium Rough,” “Medium Smooth,” “Smooth,” and “Very Smooth.”

 Thickness: Depth of the growing media layer in meters.

 Conductivity: Thermal conductivity of the growing media relating to the wet or dry

environment (W/(m-K).

 Density: Density of the dry growing media (kg/m3).

 Specific heat: Specific heat of growing media in units of kg/m3.

 Absorbance: Ratio of long wave radiation absorbed by growing media (solar, thermal,

visible).

The software uses these parameters to calculate vegetation and soil temperature as well as the

31 Photovoltaic Systems

Renewable energy application has received a great deal of attention in recent years. Solar

photovoltaic systems convert sunlight directly into electricity and generate pollution-free

energy. They can be installed on the roof as well as on the wall of commercial buildings. The

solar cells of PV systems contain light-absorbing materials in the cell structure to absorb

photons and generate electricity through the photovoltaic effect.

PV Components:

PV systems consist of several core and optional components as shown in Figure 2-6.

Figure 2-6: PV systems Components [75]

Raking Systems:

Raking systems support the PV components and attach them to the roof or to ground- mounted

construction. They must be secure and hold the PV systems under wind or snow loading.

Tracking systems:

Tracking systems increase the efficiency of PV systems by rotating the PV arrays to track sun

direction throughout both the day and year. They typically are installed on ground-mounted

32 Battery Storage Systems:

Battery storage systems are used in off-grid facilities. The extra energy generated can be stored

and used when the grid is not available. The economic viability of battery storage systems are

based on various factors such as electricity price, weather conditions, and the size of PV

pr