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Urban form and building energy : quantifying relationships using a multi-scale approach Miller, Nicole 2013

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URBAN FORM AND BUILDING ENERGY: QUANTIFYING RELATIONSHIPS USING A MULTI-SCALE APPROACH by NICOLE MILLER B.Arch., University of Kansas, 2004 M.A.S.A., University of British Columbia, 2006 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2013 © Nicole Miller, 2013 ii AbstrAct Cities and energy are fundamentally connected. Approximately half of the energy consumed in urban areas is used by buildings, resulting in over 35% of the world’s greenhouse gas (GHG) emissions. Urban form (i.e. building morphology and urban structure) plays an important role in building energy; however, few studies have addressed this role in a comprehensive and quantitative way due to the complexities involved in modeling urban systems.  This study provides a multi- scale examination of the relationships between urban form and building energy, using the Metro Vancouver region of British Columbia, Canada as an example. The thesis applies consistent methods of 3D and energy simulation modeling at three scales, ranging from individual buildings to urban patterns (i.e. neighbourhood-scale). Quantified impacts of urban form on building heating demand, cooling demand and local energy generation potential are presented. In total, 12 building archetypes and 14 urban patterns are modeled, ranging in density from 0.3 to 2.3 FAR (10 to 250 uph). Each pattern varies in building form, building arrangement, street configuration and mix of building activities. The results illustrate that building morphology and urban structure do influence building energy demand and local energy generation potential; however, the complexity and heterogeneity of urban form at larger scales is found to abate net impacts as “tradeoffs” occur between the energy-reducing and energy-increasing effects of urban form characteristics. For example, while the heating and cooling demand of individual buildings can vary significantly (30%- 70%) with urban horizon angle (an indicator of shading from adjacent buildings), the net impact modeled at the urban pattern scale is far less (as little as 5%). The results suggest that urban form will be one of many tools needed to mitigate current energy consumption and GHG emission levels, and synergies between urban form, building systems and materials, and occupant behaviour should be sought. Findings from this thesis will inform the work of local governments and urban planners with interest in building energy at scales larger than individual buildings. iii PrefAce This dissertation is original and independent work by the author, Nicole Miller. No portion of the work has been previously published. iv tAble of contents Abstract ����������������������������������������������������������������������������������������������������������������������������������������� ii Preface ����������������������������������������������������������������������������������������������������������������������������������������� iii table of contents �������������������������������������������������������������������������������������������������������������������������� iv list of tables ������������������������������������������������������������������������������������������������������������������������������� viii list of figures ������������������������������������������������������������������������������������������������������������������������������� xi List of Abbreviations ������������������������������������������������������������������������������������������������������������������� xiv Acknowledgements ��������������������������������������������������������������������������������������������������������������������� xv cHAPter 1: IntroDUctIon ���������������������������������������������������������������������������������������������������������� 1 1.1 Problem Statement ...................................................................................................................3 1.2 Research Purpose and Significance ...........................................................................................4 1.3 Research Questions ...................................................................................................................5 1.4 Research Strategy ......................................................................................................................6 1.4.1 A three part approach ................................................................................................................ 7 1.4.2 Urban form characteristics at three scales ................................................................................ 8 1.4.3 Three studies .............................................................................................................................. 9 1.5 Structure of Dissertation .........................................................................................................11 cHAPter 2: lIterAtUre reVIeW ������������������������������������������������������������������������������������������������� 12 2.1 Urban Form and Energy ..........................................................................................................12 2.1.1 Building morphology and heat transfer ................................................................................... 15 2.1.2 Building morphology and solar access ..................................................................................... 16 2.1.3 Urban structure ........................................................................................................................ 18 2.2 Urban Form Research Methods ..............................................................................................21 2.2.1 Urban patterns ......................................................................................................................... 22 2.2.2 Qualitative pattern approaches ............................................................................................... 24 2.2.3 Quantitative pattern approaches ............................................................................................. 25 2.2.4 Patterns in computer modeling applications ........................................................................... 26 2.3 Building Energy Modeling Methods ........................................................................................29 2.3.1 Building-scale energy models ................................................................................................... 30 2.3.2 Regional and national-scale energy models  ........................................................................... 31 2.3.3 Neighbourhood-scale energy models ...................................................................................... 32 2.4 Conclusion ...............................................................................................................................34 vcHAPter 3: MetHoDs ����������������������������������������������������������������������������������������������������������������� 35 3.1 Methodological Gaps in Current Research ..............................................................................35 3.2 Research Approach .................................................................................................................38 3.3 Urban Form Characteristics at Three Scales ............................................................................39 3.3.1 Geographic context .................................................................................................................. 42 3.3.2 Building archetype (BA) scale  .................................................................................................. 44 3.3.3 Local shading (LS) scale  ........................................................................................................... 49 3.3.4 Urban pattern (UP) scale  ......................................................................................................... 51 3.3.5 Urban form 3D modeling ......................................................................................................... 55 3.4 Energy Simulation ...................................................................................................................56 3.4.1 VE-Pro simulation tool .............................................................................................................. 56 3.4.2 VE-Pro outputs .......................................................................................................................... 60 3.5 Analysis of Energy and Urban Form  .......................................................................................63 3.6 Limitations of the Research .....................................................................................................64 cHAPter 4: eXIstInG UrbAn forM conDItIons ����������������������������������������������������������������������� 67 4.1 Methods ..................................................................................................................................68 4.1.1 Selection of building archetypes .............................................................................................. 68 4.1.2 Selection of existing urban patterns ......................................................................................... 69 4.1.3 Urban form 3D modeling ......................................................................................................... 77 4.1.4 Building energy simulation ....................................................................................................... 78 4.2 Results and Discussion  ...........................................................................................................79 4.2.1 Heating demand ....................................................................................................................... 79 4.2.2 Cooling demand ....................................................................................................................... 88 4.2.3 Solar potential .......................................................................................................................... 91 4.2.4 District energy potential ........................................................................................................... 94 4.3 Conclusion  ..............................................................................................................................96 cHAPter 5: bUIlDInG enerGY AnD UrbAn strUctUre������������������������������������������������������������� 99 5.1 Methods Part 1: Parametric Analysis ....................................................................................100 5.1.1 3D modeling ........................................................................................................................... 100 5.1.2 Energy simulation ................................................................................................................... 102 5.1.3 Analysis metrics  ..................................................................................................................... 104 5.1.4 Regression analysis ................................................................................................................. 104 vi 5.2 Methods Part 2: Sensitivity Testing .......................................................................................106 5.2.1 Building activity ...................................................................................................................... 106 5.2.2 Envelope performance ............................................................................................................ 107 5.3 Results and Discussion: Parametric Analysis .........................................................................109 5.3.1 Heating demand ..................................................................................................................... 109 5.3.2 Cooling demand ..................................................................................................................... 118 5.3.3 Solar potential ........................................................................................................................ 126 5.4 Results and Discussion: Sensitivity Testing ............................................................................132 5.4.1 Building activity ...................................................................................................................... 132 5.4.2 Envelope performance ............................................................................................................ 136 5.5. Conclusions ..........................................................................................................................142 cHAPter 6: UrbAn forM AlternAtIVes for bUIlDInG enerGY PerforMAnce �������������������� 145 6.1 Methods ................................................................................................................................146 6.1.1 Pattern development .............................................................................................................. 146 6.1.2 Urban form 3D modeling ....................................................................................................... 156 6.1.3 Building energy simulation ..................................................................................................... 156 6.1.4 Analysis metrics ...................................................................................................................... 157 6.2 Results and Discussion ..........................................................................................................157 6.2.1 Heating demand ..................................................................................................................... 157 6.2.2 Cooling demand ..................................................................................................................... 164 6.2.3 Solar potential ........................................................................................................................ 170 6.2.4 District energy potential ......................................................................................................... 174 6.3 Conclusions ...........................................................................................................................176 cHAPter 7: DIscUssIon AnD conclUsIons ���������������������������������������������������������������������������� 180 7.1 Summary of Results  ..............................................................................................................182 7.2 Synthesis of Results  ..............................................................................................................186 7.2.1 Research Question 1: Urban form and energy demand ........................................................ 186 7.2.2 Research Question 2: Urban form and local energy generation ........................................... 193 7.2.3 Research Question 3: Sensitivity to other factors .................................................................. 197 7.2.4 Tradeoffs ................................................................................................................................. 198 7.2.5 Summary of key findings ........................................................................................................ 200 7.3 Research Contributions and Implications ..............................................................................203 7.3.1 Implications for urban form ................................................................................................... 204 7.3.2 Implications for local governments in Metro Vancouver ....................................................... 207 vii 7.4 Research Limitations and Future Research ...........................................................................208 references �������������������������������������������������������������������������������������������������������������������������������� 212 Appendix A: building Archetypes ����������������������������������������������������������������������������������������������� 222 Appendix B: Urban Patterns������������������������������������������������������������������������������������������������������� 234 Appendix C: Modeling Assumptions ������������������������������������������������������������������������������������������ 248 Appendix D: Modeling results ��������������������������������������������������������������������������������������������������� 255 Building Archetype Results .............................................................................................................. 255 Urban Pattern Results ...................................................................................................................... 263 LS Scale Heating Demand Results - UHA Variations ....................................................................... 270 LS Scale Cooling Demand Results - UHA Variations ........................................................................ 289 LS Scale Solar Potential Results - UHA Variations ........................................................................... 308 viii lIst of tAbles Table 2.1: Urban form variables affecting energy at different scales ............................................12 Table 2.2: Energy of implications of structural variables, Owens, 1986 ........................................13 Table 2.3: Factors influencing building energy consumption ........................................................13 Table 2.4: Summary of issues involved in the description and analysis of urban form .................23 Table 2.5: Typical energy modeling approaches at different scales ..............................................29 Table 2.6: Categories of building energy tools with examples ......................................................30 Table 3.1: Urban form characteristics considered in selected building energy studies .................37 Table 3.2: Urban form characteristics at the building archetype (BA) scale ..................................49 Table 3.3: Urban form characteristics at the local shading (LS) scale ............................................52 Table 3.4: Variables included in pattern scale building energy analysis ........................................55 Table 4.1: Building archetypes and variations ...............................................................................70 Table 4.2: Preliminary patterns and urban form characteristics (qualitative) ...............................71 Table 4.3: Existing urban patterns .................................................................................................76 Table 4.4: Annual space heating demand by pattern ....................................................................80 Table 4.5: Typical residential unit size and average household size ..............................................81 Table 4.6: Annual space heating demand by building archetype ..................................................82 Table 4.7: Annual space cooling demand by pattern ....................................................................88 Table 4.8: Annual space cooling demand by building archetype ..................................................88 Table 4.9: Annual heating and cooling demand by building activity .............................................90 Table 4.10: Annual heating and cooling demand by glazing ratio (residential activity only) .........90 Table 4.11: Annual urban pattern solar potential (SP) ..................................................................92 Table 4.12: Total annual heating demand by pattern for district energy potential .......................94 Table 4.13: Proposed district energy systems in BC ......................................................................95 Table 5.1: Building archetypes included in sensitivity testing for building activity .....................106 Table 5.2: Building archetypes included in sensitivity testing for envelope performance ..........107 Table 5.3: Comparison of BC Building Code and Harmony House insulation levels ....................109 Table 5.4: Annual heating demand linear regression statistics ...................................................111 Table 5.5: Total average UHA influence on annual heating demand ...........................................112 ix Table 5.6: UHA influence on annual heating demand, select examples ......................................113 Table 5.7: UHA influence on annual heating demand, sorted by building orientation ...............115 Table 5.8: UHA influence on annual heating demand, sorted by adjacent building height ........116 Table 5.9: Effects of SR coverage on annual heating demand .....................................................117 Table 5.10: Annual cooling demand linear regression statistics ..................................................120 Table 5.11: Total average UHA influence on annual cooling demand .........................................120 Table 5.12: UHA influence on annual cooling demand, select examples ....................................121 Table 5.13: UHA influence on annual cooling demand, sorted by building orientation ..............123 Table 5.14: UHA influence on annual cooling demand, sorted by adjacent building height .......123 Table 5.15: Effects of SR coverage on annual cooling demand, select examples ........................125 Table 5.16: Annual solar potential linear regression statistics ....................................................128 Table 5.17: UHA influence on annual solar potential, select examples .......................................129 Table 5.18: UHA influence on annual solar potential, sorted by building orientation ................130 Table 5.19: Effects of SR coverage on annual solar potential, select examples ...........................131 Table 5.20: Average UHA influence on annual heating demand, building activity variations .....133 Table 5.21: Average UHA influence on annual cooling demand, building activity variations ......134 Table 5.22: Average UHA influence on annual heating demand, building activity examples ......134 Table 5.23: Average UHA influence on annual cooling demand, building activity examples ......135 Table 5.24: Average UHA influence on annual heating demand, building envelope variations ..139 Table 5.25: Average UHA influence on annual cooling demand, building envelope variations ..140 Table 5.26: Average UHA influence on annual heating demand, building envelope examples ...141 Table 5.27: Average UHA influence on annual cooling demand, building envelope examples ...142 Table 6.1: List of building archetypes ..........................................................................................149 Table 6.2: Calculation assumptions .............................................................................................149 Table 6.3: North-south oriented transit corridor patterns ..........................................................152 Table 6.4: East-west oriented transit corridor patterns  ..............................................................154 Table 6.5: Annual space heating demand by pattern ..................................................................158 Table 6.6: Annual space cooling demand by pattern ..................................................................164 Table 6.7: Annual solar potential by pattern ...............................................................................171 Table 6.8: Annual heating demand by pattern for district energy potential ...............................174 Table 6.9: Annual heating demand by pattern for district energy potential (corridor only) .......175 Table 6.10: Summary of annual energy performance by pattern................................................176 xTable 7.1 Matrix of chapters and research questions .................................................................181 Table 7.2 Matrix of urban form and building energy measures ..................................................184 Table 7.3 Relative influences of urban form characteristics on building energy .........................187 xi lIst of fIGUres Figure 1.1: Research process .........................................................................................................10 Figure 2.1: Relationship between height (h) to width (w) ratio, urban horizon angle (u) and sky view factor (Vs), adapted from Robinson, 2006 ............................................................................19 Figure 3.1 Unidirectional and multi-directional analysis of urban structure .................................37 Figure 3.2 Inputs to and outputs of building energy analysis .......................................................38 Figure 3.3: Research process .........................................................................................................40 Figure 3.4: Three scales of urban form analysis ............................................................................41 Figure 3.5: Metro Vancouver region .............................................................................................42 Figure 3.6: Roof shapes, illustrated example.................................................................................46 Figure 3.7: Glazing distribution, illustrated example .....................................................................47 Figure 3.8: Glazing ratio variation, illustrated example .................................................................48 Figure 4.1: Building archetypes and urban form characteristics ...................................................69 Figure 4.2: Urban patterns and urban form characteristics ..........................................................73 Figure 4.3: Urban pattern street configurations ............................................................................78 Figure 4.4: Annual urban pattern space heating demand per capita (GJ/cap) ..............................80 Figure 4.5: Annual urban pattern space heating demand per unit of floor area (GJ/m2)..............82 Figure 4.6: Annual building archetype space heating demand per unit of floor area (GJ/m2) ......83 Figure 4.7: Annual space heating demand per unit of floor area (GJ/m2), Provincial data ...........83 Figure 4.8: Constant residential unit, number of shared surfaces ................................................84 Figure 4.9: Surface to volume ratio and annual space heating demand .......................................85 Figure 4.10: Surface to volume ratios and glazing ratios by building archetype ...........................85 Figure 4.11: Effects of glazing ratio on annual space heating demand .........................................86 Figure 4.12: Comparison of annual space heating and cooling demand per m2 floor area for isolated and contextualised building archetypes ..........................................................................87 Figure 4.13: Annual space heating demand by number of shared surfaces and effect of envelope thermal performance ....................................................................................................................87 Figure 4.14: Annual urban pattern space cooling per capita (GJ/cap) ..........................................89 Figure 4.15: Annual urban pattern space cooling demand per unit of floor area (GJ/m2) ............89 xii Figure 4.16: British Columbia energy consumption by end use. ...................................................91 Figure 5.1: Relationship between UHA and adjacent building height .........................................100 Figure 5.2: Adjacent building locations .......................................................................................101 Figure 5.3: Number of adjacent buildings and shading range (SR), plan view ............................102 Figure 5.4: Comparison of simulation results for four and eight adjacent structures, by UHA and orientation ..................................................................................................................................103 Figure 5.5: Shading range and building widths ...........................................................................104 Figure 5.6: Annual heating demand by building vintage .............................................................108 Figure 5.7 Annual heating demand, linear regression.................................................................110 Figure 5.8 Examples of typical building arrangements and UHA ................................................114 Figure 5.9: Effects of SR coverage on annual heating demand, select examples ........................116 Figure 5.10 Annual cooling demand, liner regression .................................................................119 Figure 5.11: Effects of SR coverage on annual cooling demand ..................................................124 Figure 5.12 Annual solar potential, linear regression..................................................................127 Figure 5.13: Effects of SR coverage on annual solar potential .....................................................130 Figure 5.14: Effects of building activity on annual heating demand, linear regression ...............132 Figure 5.15: Effects of building activity on annual cooling demand, linear regression ...............132 Figure 5.16: Total annual space conditioning demand by building archetype ............................136 Figure 5.17: Effects of envelope performance on annual heating demand, linear regression ....137 Figure 5.18: Effects of envelope performance on annual cooling demand, linear regression ....138 Figure 6.1: Cambie Corridor Plan ................................................................................................147 Figure 6.2: Street configuration...................................................................................................148 Figure 6.3: Transit corridor patterns and urban form characteristics ..........................................155 Figure 6.4 Total annual corridor pattern space heating demand (GJ) .........................................158 Figure 6.5: Annual corridor pattern space heating demand per capita (GJ/cap) ........................159 Figure 6.6: Annual corridor pattern space heating demand per unit of floor area (GJ/m2) ........159 Figure 6.7: Spatialized annual heating demand (GJ/m2), north-south oriented patterns ...........162 Figure 6.8: Spatialized annual heating demand (GJ/m2), east-west oriented patterns ...............163 Figure 6.9 Total annual corridor pattern space cooling demand (GJ) ..........................................165 Figure 6.10: Annual corridor pattern space cooling demand per capita (GJ/cap) .......................165 Figure 6.11: Annual corridor pattern space cooling demand  per unit of floor area (GJ/m2) ......166 Figure 6.12: Spatialized annual cooling demand (GJ/m2), north-south oriented patterns ..........168 xiii Figure 6.13: Spatialized annual cooling demand (GJ/m2), east-west oriented patterns ..............169 Figure 6.14 Spatialized annual solar potential (Asp/Afloor), north-south oriented patterns ...........172 Figure 6.15 Spatialized annual solar potential (Asp/Afloor), east-west oriented patterns ..............173 Figure 6.16: Blocks included for DE feasibility assessment, CNS-Lin pattern example ..................175 Figure 6.17 Tradeoffs in annual heating demand, cooling demand and solar potential at the UP scale ............................................................................................................................................178 Figure 7.1: Three scales of urban form analysis ..........................................................................181 Figure 7.2 Inputs to and outputs of building energy analysis .....................................................182 Figure 7.3: Effects of glazing ratio on annual space heating demand .........................................188 Figure 7.4: Surface to volume ratio and annual space heating demand .....................................189 Figure 7.5 Impacts of UHA at the LS scale on annual heating and cooling demand (IC%), by building archetype ......................................................................................................................191 Figure 7.6 Impacts of UHA at the LS scale on annual heating and cooling demand (IC, MJ/m2), by building archetype ......................................................................................................................191 Figure 7.7 Examples of differences in solar potential at the LS scale by roof shape and building orientation ..................................................................................................................................194 Figure 7.8 FAR and annual solar potential for modeled urban patterns .....................................196 Figure 7.9 Tradeoffs in annual heating demand, cooling demand and solar potential at the UP scale ............................................................................................................................................200 xiv lIst of AbbreVIAtIons The following list identifies key abbreviations and acronyms used throughout the dissertation. Abbreviations used to identify building archetypes and urban patterns discussed in the dissertation are identified separately in Appendices A and B. 3D  three-dimensional ASHRAE American Society of Heating, Refrigerating and Air Conditioning Engineers BA  building archetype (scale) BC  British Columbia BCBC  British Columbia Building Code BES  building energy simulation DE  district energy DEM  digital elevation model FAR  floor area ratio GHG  greenhouse gas GIS  geographic information system HR  height ratio IC  influence coeffecient LS  local shading (scale) LSRP  Livable Region Strategic Plan LT method Lighting and Thermal method RGS  Regional Growth Strategy SP  solar potential SR  shading ratio S:V  surface to volume ratio UHA  urban horizon angle UHI  urban heat island UP  urban pattern (scale) UPH  units per hectare xv AcknoWleDGeMents I would like to express my gratitude to my supervisory committee: Dr. Stephen Sheppard (UBC Institute for Resources, Environment and Sustainability), Professor Ron Kellett (UBC School of Architecture and Landscape Architecture) and Dr. Jeff Carmichael (Metro Vancouver). Their diverse expertise and insight took my research in directions I would have never imagined at the outset. I am further indebted to Stephen Sheppard for the opportunity to work on a number of exciting projects throughout the course of my doctoral studies and to Ron Kellett for helping me to lay the groundwork for this research over eight years of enjoyable and enlightening learning and collaboration. Thanks to my friends and colleagues at IRES for their support and welcome distractions, my workmates and dear friends Duncan, Ellen and Sara who have cheered me on throughout this process and kept me engaged in the professional side of things, and to my parents for their constant support and advice – it’s so much nicer having you right here in the northwest. Finally, thanks to Courtney for his patience, hard work, and hours of discussion and comparing notes – as I said the last time I wrote one of these, I would not and could not do it without you. This work was made possible with generous funding from the University of British Columbia, the Pacific Institute for Climate Solutions, the Lincoln Institute of Land Policy and the Mitacs Accelerate Internship Program. 1cHAPter 1: IntroDUctIon Cities and energy are fundamentally connected. While urban areas account for roughly 2% of the earth’s surface area, they account for three-quarters of the world’s energy consumption (Salat, 2008). Of the energy consumed in urban areas approximately half is used in the operation of buildings, resulting in over 35% of the world’s greenhouse gas (GHG) emissions (Price, et al., 2006). With this magnitude of global impact, the building sector has been identified as a key component in the mitigation of climate change, in British Columbia and internationally. Energy demand in buildings is affected by a variety of factors. Mitchell (2005) lists eight factors for building energy consumption, including the thermal performance of construction materials, the efficiency of building systems, occupant activities, building morphology and urban geometry. Of these factors, materials (e.g. insulation) and technology (e.g. high-efficiency building systems) at the scale of individual buildings have been a primary focus of research, programs and policy initiatives for building energy demand reductions and building-related GHG emissions mitigation. However, literature reviewed for this project suggests that urban form – both the morphology of individual buildings and the urban structure in which they are located – also plays an important role in building energy. Urban form affects building energy both directly through specific physical attributes (e.g. surface to volume ratio) and indirectly through the facilitation of technology (e.g. solar energy technologies and district energy systems). Ratti et al. suggest that urban form, particularly the relationships between buildings, may account for a factor of two difference in building energy performance (2005). Baker and Steemers (2000) offer two compelling reasons why urban form should be addressed when considering opportunities to reduce building energy consumption. First, urban form factors are less likely to change than technologies and occupants, such that urban form elements are likely to have much longer term positive (or negative) consequences. Second, they suggest that form, technology and occupants do not act independently; rather, good urban form may result in better system performance and occupant behavior. Wener and Carmalt (2006) suggest that form-based design choices such as compact building shapes are less dependent on, or susceptible to, behavioural changes than technologies requiring occupant interaction. The consideration of urban form as a mechanism for reducing building energy demand is particularly relevant to local governments in British Columbia, where the establishment of GHG targets and the development of related policy have increasingly mandated local government action on climate change, primarily through the building and transportation sectors. 2The Province of British Columbia has recently estimated that 45% of total GHG emissions in the Province are either controlled or influenced by local governments (Province of British Columbia, 2010).  However, local government has also been largely limited in its power to act on energy and climate issues. Bulkeley and Kern (2006) discuss the challenges faced by local governments in both the UK and Germany in instituting climate change policies. Numerous authors have noted the same challenge for local governments in British Columbia where the Local Government Act limits municipal authority, making it difficult or impossible for municipalities to regulate issues such as renewable energy or energy efficiency in buildings (Fraser Basin Council, 2007; Energy Efficiency Working Group, 2008; Gore et al., 2009; McDowall, 2008; Union of British Columbia Municipalities, 2007). With limited ability to mandate building energy efficiency, local governments in British Columbia desiring to reduce building energy demand and GHG emissions must instead rely on available planning and land use zoning powers, of which urban form – not building materials or technology – is the primary opportunity. If urban form is a primary opportunity for local governments to reduce building energy demand, new and better ways of understanding urban form and planning decisions in terms of energy and GHG emissions are required. Existing literature identifies that urban form is a strong influence in urban energy consumption and the generation of GHG emissions, potentially determining urban energy performance to a greater degree than small-scale choices (Mehaffy et al., 2009; Ratti et al., 2005). However, describing and quantifying the specific energy and GHG impacts of urban form, particularly at the neighbourhood and larger scales, are challenging due to the many factors and complex interactions involved in these systems. Such challenges have resulted in a limited number of studies addressing urban form- energy relationships in a comprehensive and quantitative way. This thesis provides a multi-scale examination of the relationships between urban form characteristics and building energy performance, using 3D urban form modeling and building energy simulation tools. Building on the work of several important urban form-building energy studies, the following chapters present quantitative findings on the impacts of urban form on building heating and cooling demand and local energy generation potential. Using climatic data and urban form conditions specific to the Metro Vancouver region of British Columbia, Canada, the thesis explores the magnitude of change in building energy demand that could potentially be realised through changes in urban form within the Metro Vancouver climate and urban form context. 31�1 Problem statement Despite increased awareness that “green communities” are an essential component of energy reduction and GHG mitigation (Province of British Columbia, 2008), local governments have not yet bridged substantial gaps between stated energy and GHG reduction goals, planning practice and implementation, including the development of urban forms responsive to building energy issues.  While it can be argued that the present inertia of energy and emissions-intensive patterns of development is due in large part to implementation limitations (e.g. economics, community resistance, etc.) there is also a lack of clear, quantifiable and spatial information on specific relationships between urban form choices and energy and GHG consequences. This information is critical to the provision of solid rationale and support for effective energy and emission policies and their implementation. Literature reviewed for this project finds that neighbourhood-scale modeling capturing the effects of urban form characteristics on building energy demand is especially lacking (Barton, 2004), and that work at the neighbourhood scale tends to be limited in scope, issue-specific and methodologically less developed than approaches at either the building or regional scales. At the same time, the neighbourhood scale is critical to the understanding of urban form-energy relationships, as it captures key interactions between buildings that are not typically represented at smaller or larger scales. The problem of modeling energy and urban form is spatial, multi-scale and multi-issue, encompassing factors including not only form, but also technology, climate and behaviour, among others. While building-scale energy simulations have sought to incorporate many of these factors into comprehensive, data-intensive and time consuming models, the trend at the neighbourhood scale has been to reduce complexity through the use of archetypes, averages and assumptions for many building-scale and contextual factors in order to highlight the impacts of urban form and planning decisions. This has sometimes led to the critique that such models are not sufficiently accurate (Robinson, 2006). Urban form-energy models are further challenged to reflect the performance of urban form choices under a variety of future conditions in ways and through methods that are defensible and valid. Most urban form-based work on neighbourhood-scale energy modeling has evaluated only existing conditions, potentially due to difficulties in generating future form-based data at sufficient levels of detail. The availability of approaches able to spatially describe future urban form and energy conditions in ways that are measurable, as well as understandable and relevant to policy makers, is arguably the largest gap in current research. 4Comparative studies conclusively illustrate that building and neighbourhood-scale urban form choices impact building energy consumption, but a lack of consistent approaches and typically small sample sizes make further conclusions on particular urban form characteristics and energy demand difficult. In addition, available literature has tended to separate morphological factors from land and building uses, despite the emphasis by Baker and Steemers (2000) on the important differences between residential and commercial building energy patterns and performance. Research in the academic realm has largely dealt with abstract tests of morphological conditions (e.g. Cheng et al., 2006) or detailed morphological analysis of very small subsets of urban form typologies (e.g. Salat, 2009; Ratti et al., 2005). A large number of government and applied research projects rely on the use of residential unit type (e.g. single family detached, apartment) as a placeholder for urban form characteristics, likely due to the greater availability of residential unit data and the difficulties of modeling complex urban geometry (Ratti et al., 2005). As a result, building morphology and urban structure are not easily characterised in these studies. Numerous authors note that further research on urban form-building energy relationships, including multivariate analysis (Mitchell, 2005), improved analytical approaches (Steemers, 2003), and simplified energy tools able to evaluate form at early stages of design (Baker and Steemers, 2000) are needed. This dissertation suggests that to support local governments in achieving energy and GHG emission reduction targets, the role of urban form in building energy demand must be addressed in greater detail, as urban form will be a primary local government lever in situations where the power to regulate building systems and materials is limited, such as in British Columbia. More research is needed to explore specific relationships between energy and urban form at a variety of scales and within specific regional and climatic contexts, including tradeoffs between and sensitivity to a variety of urban form characteristics, rather than more general relationships. 1.2 Research Purpose and Significance The purpose of this research is to provide an in-depth examination of the relationships between urban form characteristics and building energy performance to better inform urban planning and development decisions, using the Metro Vancouver area of British Columbia, Canada as an example. Extending from the literature described in Chapter 2, the research centres on the use of 3D and building energy simulation modeling tools to quantify building energy impacts of and interactions between urban form characteristics at the building and neighbourhood scales, concentrating on building morphology and urban structure. 5Findings from this research have the potential to inform the work of local governments, urban planners and designers, energy planners and others with interest in reducing building energy demand at scales larger than individual buildings. While the findings presented in the following chapters are specific to the Metro Vancouver climate and context, the methodological approach to the project, which pairs an archetype-based method of describing urban form with commercially available 3D modeling and building energy simulation tools to compare urban form alternatives at multiple scales, is applicable in many contexts. This research uniquely uses consistent methods of 3D and energy simulation modeling across building energy studies at multiple scales, allowing for a greater understanding of both the parts and the whole of the urban form-energy system. Lessons learned at smaller scales of inquiry assist in providing explanations for results that would be otherwise challenging to understand at larger scales. Importantly, the multi-scale methods used in this research provide a foundation for discussing the unexpectedly small effects of urban form on building energy at the neighbourhood scale found in some studies (e.g. Ratti et al., 2005). 1.3 Research Questions The following three research questions will structure the work. The research approach developed to answer these questions is presented in the remainder of this chapter and in Chapter 3. 1) How do urban form characteristics affect building energy demand in Metro Vancouver? a. What are the quantitative differences in building energy demand among existing urban form conditions? b. What changes in building energy demand might be achieved through the manipulation of urban form for future development? Question 1 can be considered the primary focus of the research. To answer this question, the impacts of key urban form characteristics on building energy demand are examined through 3D and building energy simulation modeling at three scales. While research and policy often discuss generalised benefits of urban form characteristics such as building shape, glazing, density and mixed uses for building energy performance, detailed studies isolating and quantifying the effects of these and other urban form characteristics occur less frequently. Specifically, the spatial arrangement (i.e. urban structure) of density and land use is often neglected. Based on a review of existing literature (Chapter 2), the hypothesis of this dissertation for Research Question 1 is that targeted changes in urban form characteristics will affect heating and cooling 6demand in individual buildings. However, at larger scales, it is anticipated that the complexity of urban form conditions will create energy-related “tradeoffs” that limit the overall potential to reduce building energy demand. 2) How do urban form characteristics affect capacity for local energy supply in Metro Vancouver? To more fully address the potential impacts of urban form on building energy, the effects of urban form characteristics on the potential for building energy supply should also be considered. At the building and neighbourhood scales of urban form considered in this research, localised energy supply options that may be impacted by changes in urban form are of particular interest. Within the scope of this project, solar energy potential and district energy potential (Section 3.4) are modeled as key local energy supply options impacted by urban form choices. As in the hypothesis for Question 1, it is expected that targeted changes in urban form characteristics will affect the potential for local energy generation, and that energy-related “tradeoffs” between urban form characteristics will limit local energy generation potential as the complexity of urban form increases. For example, tradeoffs between density, building spacing and solar access have been noted by a number of studies (Baker and Steemers, 2000; Cheng et al., 2006; Ratti et al. 2005; Compagnon, 2004; Carneiro et al., 2008). 3) How do other factors affecting building energy, such as building activity (e.g. residential, commercial) and building envelope performance, impact the effects of urban form characteristics on building energy demand? The purpose of Question 3 is to further contextualize the impacts of urban form on building energy demand through targeted, energy simulation-based sensitivity testing. This question is intended to be secondary to Questions 1 and 2 and will focus on a subset of the selected urban form conditions for comparison purposes. 1�4 research strategy The intent of this research is to provide a more comprehensive understanding of the relationships between urban form and building energy to better inform urban planning and development decisions. Towards that end, the project examines several identified urban form characteristics: building shape; glazing; orientation; and urban structure (i.e. building spacing and arrangement) (see Chapter 3 for rationale). To achieve this, the thesis addresses urban form and building energy 7using a three part approach (Section 3.1), applied to three scales of urban form (Section 3.2) and completed over three separate but related studies (Chapters 4, 5 and 6). 1�4�1 A three part approach To fully understand the relationships between energy and urban form, one must consider the means by which to describe and measure urban form, the means by which to describe and measure energy and the means by which to discern relationships between the two. A general framework for this approach is described by Steinitz (1990) who presents six, iterative levels of inquiry, applicable to a number of design and planning problems. Adapting Steinitz’s approach, the methodology used for this project is comprised of three key parts: 1) To understand and describe existing urban conditions and possible changes, urban form characteristics at three scales (Section 3.2) were selected and modeled using 3D modeling software, providing the quantitative and spatial urban form data for the project. 2) To evaluate the building energy performance of existing urban conditions and possible changes, each 3D urban form model was imported to and analysed in building energy simulation software (Section 3.3), providing energy performance data. 3) Using the urban form data as independent variables and the energy performance data as dependent variables, sensitivity, regression and other analyses were completed to compare urban form characteristics and to draw conclusions regarding the relationships between urban form decisions and building energy (Section 3.4). 1.4.1.1 Urban form 3D modeling Urban form conditions at three scales were modeled three-dimensionally using SketchUp (sketchup. google.com), a 3D building modeling software widely used for architecture, urban design and other applications. SketchUp was chosen for this research based on its ability to quickly model 3D building forms, and the availability of software plug-ins that allow building geometry created in SketchUp to be directly imported to building energy simulation programs. Further information on the 3D modelling approached used for this research can be found in Section 3.2.5. 1.4.1.2 Building energy simulation Energy simulations were carried out at three scales using VE-Pro, described in detail in Section 3.3. Standard assumptions were used as inputs into the energy simulations for all building characteristics not related to urban form, such as the thermal properties of building envelopes and lighting and appliance energy loads. 81.4.1.3 Data analysis Using the urban form measures described in Section 3.2 as independent variables and the VE- Pro outputs (i.e. energy performance data) described in Section 3.3 as dependent variables, relationships between urban form and building energy were able to be described and quantified at three scales. Each study (Chapters 4 through 6) considers a different scale or combination of scales of urban form; consequently, each study also applies different approaches to the analysis of results. For example, in Chapters 4 and 6 only a small number of urban form conditions (i.e. 12 building archetypes and six urban patterns) were modeled. For this small number of conditions, additional statistical analysis (e.g. regression analysis) was not completed; rather, comparisons were drawn and trends identified across modeled building archetypes and urban patterns individually. In Chapter 5, linear regression was used to establish urban form-energy trends over a much larger data set, resulting from the completion of a parametric analysis. R-squared values were calculated for each relationship to quantify relationship strength. Influence coefficients (IC) were calculated for each urban form measure and associated energy outcome to “quantify the influence of one variable on another” (Spitler et al. 1989). Chapter 5 also includes additional sensitivity analysis, testing how regression results changed as building activity and building envelope performance changed. 1.4.2 Urban form characteristics at three scales This project applies modeling and analysis methods at three scales, building from a number of research precedents described in Chapter 3: individual building archetype (BA); building archetype with local shading (LS); and urban pattern (UP). Examining urban form and building energy at multiple scales enables a more comprehensive analysis of urban form effects on building energy. Each of the three scales has distinct benefits and limitations for building energy analysis. The BA scale enables the impacts of building morphology (i.e. building shape, glazing and orientation) on building energy to be isolated and quantified. However, in application, buildings are rarely isolated from the impacts of adjacent structures, such that consideration of the BA scale alone does not provide sufficient information to assess relationships between urban form and building energy. In total, 12 building archetypes were developed for the project, with many of the building archetypes carried forward for use at the LS and UP scales. The building archetypes, detailed modeling methods and study findings at the BA scale are described in Chapter 4. The LS scale refers to an individual building archetype together with the structures directly adjacent to each building archetype facade. This scale was used for a series of parametric analysis studies, maximising the number of urban form variations considered. In total, 1,656 model runs 9were completed at the LS scale, including initial simulations as well as a variety of sensitivity tests for variations in building activity and building envelope performance. However, the LS scale still does not capture the full complexity of urban form conditions at larger scales. Specific methods and findings from the parametric analysis studies are presented in Chapter 5. Findings from the parametric analysis were used to inform choices regarding the development of the urban patterns modeled in Chapter 6. Urban patterns (i.e. UP scale), for the purposes of this project, can be defined as spatially explicit representations of urban form, assembled from building archetypes and representative street configurations, and capturing key attributes of complex urban structure over a larger land area. Unlike the BA and LS scales, the UP scale enables the study of complex urban structures representative of real-world urban development, including building arrangements within street patterns, varying densities of development and varying building shapes and orientations. However, at the UP scale, building morphology, urban structure and relationships to building energy become more difficult to describe and quantify, as many different conditions may be occurring within the same urban pattern. Further, the urban pattern (UP) scale models necessary to capture complexity involve substantial processing times for energy simulation, limiting the number of urban patterns that can be realistically considered within the scope of a project. The UP scale is included in two chapters (Chapters 4 and 6). In Chapter 4, urban patterns are used to describe and analyse existing urban form conditions within the Metro Vancouver region. In Chapter 6, the UP scale is used to apply key findings from the research to evaluate infill development options along a transit corridor. Specific rationale, methods and findings from the UP scale analyses are presented in Chapters 4 and 6. 1�4�3 three studies To support a multi-scale analysis of urban form-energy relationships, the research has been undertaken through three related studies, each captured in a separate chapter (Chapters 4, 5 and 6). Each study examines one or more scales of urban form, and lessons from each study are fed into subsequent parts of the research (Figure 1.1). Consistent methods of 3D and energy simulation modeling are applied across all three scales of urban form; however, as each study considers a different scale or combination of scales of urban form, each study applies different approaches to the analysis of results. In Chapter 4, both the building archetype (BA) and urban pattern (UP) scales are examined for existing conditions in the Metro Vancouver region. This component of the research was undertaken as an initial testing and exploration of existing regional conditions as well as the modeling methods 10 12 building archetypes 6 existing urban patterns 8 new pattern variations Parametric studies URBAN FORM STUDIES ENERGY SIMULATIONS LEVELS OF INQUIRY 1 2 3 4 4 Representation of existing conditions Understanding of existing system Evaluation of existing system Development of alternatives Evaluation of alternatives Recommendations for change 1 2 3 4 5 6 5 6 Urban Pattern (UP) Local Shading (LS) Building Archetype (BA) Chapter 4: Existing urban form conditions Chapter 5: Building energy and urban structure Chapter 6: Urban form alternatives for building energy performance Energy simulation completed in IES VE-Pro figure 1�1: research process 11 developed for the project, including both urban form (3D) modeling and building energy simulation (BES). The results from Chapter 4 represent early and, at times, incomplete findings and should be considered as an initial step in identifying patterns and potential relationships to be considered further in subsequent chapters. In Chapter 5, regression analysis is used to establish urban form-energy trends resulting from parametric analysis completed at the local shading (LS) scale. This study was used to maximise the number of urban form variations that could be considered within the timeframe of the project by examining strategic, incremental urban form variations at an intermediate scale (larger than individual buildings, but smaller than urban patterns) which reduced simulation times. Chapter 5 focuses on the impacts of building spacing and arrangement (as measured by urban horizon angle) on building heating demand, building cooling demand and solar potential. Findings from this chapter were used to inform choices regarding the urban patterns modeled in Chapter 6. Chapter 6 applies lessons from the previous chapters in the development and modeling of eight urban patterns representing infill development options along a transit corridor. The eight urban patterns are evaluated for energy performance, from which conclusions are drawn regarding the potential of urban form to affect building energy performance within the limitations of a specific development context, bounded by constraints (e.g. zoning regulations) on development density, building forms, street configurations and building activities. 1.5 Structure of Dissertation The dissertation is divided into seven chapters.  Following the Introduction (Chapter 1), Chapter 2 presents a summary of relevant literature covering three themes: the relationship between urban form and building energy; urban form research methods; and building energy modeling methods. Chapter 3 describes the methodology employed for the project, including the approaches to urban form modeling and building energy simulation. Chapter 4 identifies existing urban conditions within the Metro Vancouver region, as defined by six patterns of urban development and 12 building archetypes, and discusses results from energy simulations conducted early in the project. Chapter 5 builds on the urban form and energy literature by presenting the quantitative results for LS scale parametric analysis of urban form characteristics. Chapter 6 applies the lessons learned in the parametric analysis to neighbourhood-scale urban form choices, presenting eight urban pattern variations representing infill development options along a transit corridor. Energy simulation results for each pattern variation are provided. The dissertation concludes with Chapter 7, which summarizes the key findings from the research and extends lessons learned to recommendations for urban planning and development. 12 cHAPter 2: lIterAtUre reVIeW This chapter presents a summary of the literature reviewed in preparation for this research. Section 2.1 examines literature on the current understanding of relationships between urban form and building energy, identifying that these relationships centre on heat transfer and solar access, which are affected by both building morphology and urban structure. Section 2.2 reviews varying approaches to the study of urban form generally, focusing on “pattern” based qualitative and quantitative methods. Section 2.3 reviews building energy modeling methods, ranging from the simulation of individual buildings to regional and national scale data aggregation methods. Of particular note is a lack of robust building energy analysis methods appropriate for neighbourhood- scale studies. 2�1 Urban form and energy The relationships between urban form and energy have been explored from a variety of perspectives over several decades. Urban form variables affecting energy use, from the building to regional scales, have been identified by Owens, 1986 (Table 2.1). Owens further attempts to quantify the magnitude of potential impacts on energy consumption related to these variables (Table 2.2). The energy demand in buildings is affected by its own set of factors. Mitchell (2005) lists eight factors for building energy consumption, including occupant activities, urban geometry and thermal properties of construction materials. These characteristics are echoed by others (Table 2.3). Mitchell, as well as Salat (2009) extends energy-related factors to greenhouse gas (GHG) generation Structural Variable Scale Settlement pattern Communication network between settlements Size of settlement Shape of settlement Communication network within settlement Density Interspersion of land uses Degree of centralisation of facilities Layout Orientation Siting Design Region Settlement Orientation Building Table 2.1: Urban form variables affecting energy at different scales, adapted from Owens, 1986 13 by including determinants of energy source. Consistent within the literature is the notion that urban form – both the morphology of individual buildings and the urban structure in which they are located – contributes in part to building energy consumption. While building energy is affected by much more than urban form, Baker and Steemers (2000) offer two compelling reasons why urban form should be considered and addressed. First, urban form factors are less likely to change than technologies and occupants, so design elements are likely to have much longer term positive (or negative) consequences. Second, the authors suggest that form, technology and occupants do not act independently. Rather, good design may result in better system performance and occupant behaviour. Wener and Carmalt (2006) have also noted that design choices, such as passive design, are less dependent on, or susceptible to, behavioural changes. Structural Variable Mechanism Energy Implications Shape travel requirements variation of up to about 20% Interspersion of activities travel requirements (trip length) variation of up to 130% Combination of structural variables  (shape, size, land use mix, etc.) travel requirements (trip length and  frequency) variation of up to 150% Density/built form surface area to volume ratio affects  energy requirements for space heating 200% variation between different  building forms Density/clustering of trip ends facilitates running of public transport  system energy savings of up to 20% Density/mixing of land uses facillitates introduction of energy‐ efficient CHP/DH systems efficiency of primary energy use  improved by up to 100% Density/siting/orientation and  landscaping maximises potential to use "free"  ambient energy sources can reduce conventional energy  consumption by at least 20% Table 2.2: Energy of implications of structural variables, Owens, 1986 Table 2.3: Factors influencing building energy consumption Mitchell, 2005 Salat, 2009 Ratti et al., 2005 Urban geometry Urban geometry Building morphology Thermal performance of materials Building performance Efficiency of internal systems Equipment and system efficiency Systems efficiency Occupant activity and behaviour Inhabitant behaviour Occupant behaviour Fuel price Opportunity to share infrastructure Internal and external temperature Urban form Type of energy Building design 14 Studies considering the overall impacts of urban form on building energy present several key findings, although the magnitude of impacts differs between sources. Baker and Steemers (2000) find that building design (i.e. building morphology and envelope characteristics) accounts for a factor of 2.5 variation in energy consumption, while systems efficiency and occupant behaviour each account for a factor of 2 variation.  These effects cumulatively result in a factor of 10 difference in energy performance among buildings, but actual energy consumption in buildings can range by a factor of 20, even among buildings with comparatively similar functions (Ratti et al., 2005).  Ratti et al. postulate that urban structure, particularly the relationships between buildings, may account for the additional factor of 2 differences in performance (2005). Their study, however, is only able to account for a 9% difference in energy consumption, despite changes in building and neighbourhood characteristics.  Other studies (e.g. Johnston et al., 2005) assert that the impact of urban form characteristics is relatively small in comparison to thermal and building system performance, particularly for larger, aggregate studies. A survey of the literature suggests that, at whatever magnitude of influence, urban form plays a role in building energy consumption within two predominant spheres of urban form characteristics: building morphology and urban structure, and in two key ways: heat transfer and solar access. Building morphology refers to the size and shape of a building, including plan, elevation and sectional characteristics. Building morphology can also include facade design characteristics such as the amount and distribution of glazing and building orientation (Mitchell, 2005). Common measures of building morphology include surface to volume ratio (S:V), plan depth, building height, passive area and glazing ratios (Steadman et al., 2009; Batty et al., 2008; Baker and Steemers, 2000). Several of these measures are further described in the remainder of Section 2.1. Urban structure refers to the arrangement and spacing of (i.e. relationships between) urban elements including buildings, streets and open spaces (Mitchell, 2005; Steemers, 2003; Ratti et al., 2005; Cheng et al., 2006). Common measures of urban structure include density measures such as floor-area ratios (FAR) and land coverage, building spacing, urban horizon angles (UHA), sky view factor and others. Several of these measures are further described in the remainder of Section 2.1. Building morphology and urban structure and their effects on heat transfer and solar access will shape the discussion for the remainder of this section. The building morphology and urban structure characteristics and measures used for this research are described in detail in Chapter 3. 15 2�1�1 building morphology and heat transfer Among the morphological characteristics of buildings that have been shown to influence building energy consumption, compactness (Mitchell, 2005) and building density are primary factors. A variety of researchers note that more compact, higher density buildings typically consume less energy on a per capita or per area basis (e.g. March, 1972; Steemers, 2003; Miller and Cavens, 2008; Salat, 2009; Cobalt Engineering, 2009). At the building scale, compactness has the predominant effect of reducing heat transfer, as more compact building shapes enclose more building volume with less surface area through which heat can escape. March (1972) mathematically describes compactness using surface to volume ratios (S:V), a measure later echoed by Owens (1986) and others (e.g. Ratti et al., 2005; Salat, 2009; Steemers, 2003; Knowles, 1974). Studies attempting to quantify the impact of building compactness on energy consumption vary significantly in both approaches and findings. For surface to volume ratios, Owens (1986) suggests that building energy can vary by as much as 200%, while Ratti et al. find an inverse relationship between surface to volume ratios and energy consumption in the comparison of non-domestic buildings in three European neighbourhoods. Across the literature on building density and building energy, a variety of representations of density have been utilised. The building density-energy relationship has been described by relating energy consumption to density measures such as units per hectare (e.g. Canmet Energy, 2009), floor area ratio (e.g. Steemers, 2003) and residential unit type (e.g. single family detached, attached and apartments) (Owens, 1986; Miller and Cavens, 2008). Used in reference to a single building, density measures are often used to imply a relationship to compactness (although this is not always the case), and also reflect the effects of shared walls and floors and smaller unit sizes on energy demand. In a study examining FAR for commercial buildings, Steemers (2003) finds potentials for a 50% reduction in heating demand at increased building densities. In the same paper, Steemers also finds that doubling building density, measured by FAR, can increase total energy demand per square meter by 25% in office buildings. Studies of building density by residential unit type have been somewhat more consistent.  In their 2008 report, Ewing and Rong conclude that households living in low-density, single family homes consume 54% more energy for space heating and 26% more for cooling than a similar household living in an attached, multi-family dwelling.  This is consistent with Canadian findings based on data from the BC Hydro Conservation Potential Review (Miller and Cavens, 2008) and Natural Resources Canada (2009) which show that attached residential units such as rowhouses use approximately half the heating energy of the average single family house, while apartment units use between 65% 16 and 75% less heating energy on a per unit basis.  The difference in performance is due both to the thermal efficiencies of shared walls and floors between units and also to the smaller average unit sizes for attached and apartment dwellings. Comparative studies relating density and urban structure to building energy at the neighbourhood scale provide correlations between increased development density and reduced neighbourhood energy demand, but at varying degrees of intensity. Comparisons between studies are difficult, as the ranges of development density considered in each study differ substantially. Salat (2009) finds that for three neighbourhoods ranging in density from 13-32 units per hectare, energy consumption varied by 44% per square meter; however, the case studies also included numerous other factors, including a wide range of neighbourhood ages and building construction types that also affect results. Comparisons of neighbourhoods completed as part of Canmet’s Urban Archetypes (2009) project show that energy use can vary by 33% per capita, with a strong relationship between increased development density and lower energy consumption. While increasing compactness and building density generally reduce building heat loss, increasing these characteristics does not guarantee overall building energy reductions. The relationships between compactness, building density and building energy are complicated by differences between residential and commercial/institutional uses. Residential building energy is dominated by heating demand. Increasing building density from detached housing to apartments can reduce heating energy demand; however, such density increases typically require increased building depth, increased building height or reduced building spacing that limit access to passive heating and daylighting (see Section 2.1.2). In certain contexts, low-density residential design with high surface to volume ratios maximised for passive solar heating may provide greater opportunities than density for reduced energy demand (Steemers, 2003). Conversely, commercial and institutional buildings are dominated by lighting and cooling energy demands. For these buildings, shallow floor plans (12- 15 meters) are the most critical characteristic. Models examining increased density in commercial office buildings have shown potential overall increases in energy consumption from increased cooling demands, due to the high levels of internal heat gains present in these buildings (Baker and Steemers, 2000; Mitchell, 2005; Steemers, 2003). Steemers concludes that the arguments for and against density for building design are “finely balanced” and dependent on local contexts. 2�1�2 building morphology and solar access Density and compactness alone do not account for all building morphology related variation in energy demand. As suggested in the previous sections, an overemphasis on minimising building surface area to reduce heat loss neglects other energy issues and may not reduce total energy 17 consumption, as the availability of daylight and solar energy (i.e. solar access) also have significant impacts on residential and commercial building energy use (Baker and Steemers, 2000; Ratti et al., 2005). Arguments for and against density, balancing the need to reduce heat loss with the need for solar access, are complex (Baker and Steemers, 2000; Steemers, 2003) and additionally affected by building use, building design, available infrastructure, local contexts and specific design intent (for example, passive versus active solar energy strategies). Tradeoffs between density and solar access for residential buildings are relatively balanced, while increasing the density of office spaces tends to have negative energy consequences. At the same time, these conclusions become less certain as the complexity of surrounding urban forms increases (Steemers, 2003). In the study by Ratti et al. (2005), more compact buildings (by surface to volume ratio) were associated with higher energy consumption; greater access to light and air, measured through the quantification of passive building area, was found to be a better predictor of reduced energy consumption for non-domestic buildings. The study notes, however, that such findings are likely context specific. Steemers (2003) explains many of the potential tradeoffs between density and solar access, suggesting there may be thresholds beyond which density does not improve energy performance. In terms of building morphology, energy reduction strategies such as passive heating, daylighting and natural ventilation as well as renewable energy strategies such as solar hot water rely heavily on increased surface area, in opposition to the decreased surface area required for reduced heat loss. Increasing the amount of building surface area increases the proportion of “passive” building floor area (Baker and Steemers, 2000), or the area with the ability to utilize daylight, sunlight and natural ventilation. The proportion of building area in passive zones indicates the potential of buildings to use passive energy strategies (Baker and Steemers, 2000; Ratti et al., 2005). Passive areas are typically considered to be within six meters of the building perimeter, although this dimension varies with floor to floor height, and maximised passive areas require shallow building depths (approximately 10-12 meters). To compensate for greater heat loss potential due to increased surface areas, improved insulation standards are typically recommended for passively designed buildings (Steemers, 2003). The concept of passive areas is today widely referenced at both the building and neighbourhood scales as an essential characteristic in relating urban form to energy performance (e.g. Steemers, 2003; Ratti et al., 2005; Steadman, 2000; Salat, 2009), although less frequently  applied at the neighbourhood scale for research applications. A limited number of studies comparing amounts of 18 passive area at the neighbourhood scale suggest energy impacts ranging between 10% (Ratti et al., 2005) and 40% (Salat, 2009) per square meter of building area. Beyond passive building area, building morphology also affects solar access through facade glazing and building orientation. Optimised glazing ratios have the potential to reduce energy consumption in a building’s passive zones by an additional 6-7% beyond standard building practices (Ratti et al., 2005). However, increased glazing also incurs greater heat losses (Baker and Steemers, 2000); compensatory strategies such as increased glazing insulation standards and thermal massing for heat storage must be considered in parallel. Data from a study of mid- and high- rise residential buildings conducted for the City of Vancouver show that energy consumption can vary by 33% per square meter, with a relationship between lower glazing ratios and lower energy use (RDH Building Engineering Ltd., 2009). Optimal glazing strategies will balance solar gains and heat losses (Baker and Steemers, 2000), reduce glazing on higher portions of building facades and vary in response to building orientation (Steemers, 2003; Compass Engineering, 2009). Building orientation, or the direction in which the main building facade faces, also has significant impacts on passive strategies and is determined by street or block configuration as well as building design. A comparison of orientations in the four cardinal directions in the UK suggests that daylight availability can vary by as much as 20% (Baker and Steemers, 2000). For space heating, glazing oriented to the east and south provide the most useful solar gains, in comparison to western orientations that provide solar gains late in the day when temperatures are at a maximum and buildings are already heated (Baker and Steemers, 2000). Steemers (2003) finds that glazing with southern orientations can reduce heating energy demand an additional 11% over western orientations. Building orientation will have less of an impact in urban areas, where a higher number of obstructions reduce differences in the amount of solar and light access (Steemers, 2003). Paired with the considerations for reducing heat loss above, the optimum building morphology for energy efficient buildings would ideally increase compactness while ensuring limited building depths, strategic glazing patterns and appropriate orientation to provide maximum opportunities for passive design and renewable energy strategies (Steemers, 2003). 2�1�3 Urban structure Beyond building morphology, urban structure characteristics establishing relationships between buildings (e.g. spacing, arrangement) also play a role in determining energy consumption (Mitchell, 2005; Owens, 1986). These are particularly relevant in considering solar access (Ratti et al., 2005), for which a small subset of neighbourhood-scale energy literature exists.  At the neighbourhood 19 scale, solar access is influenced primarily by building spacing and arrangement, which are measured in several ways including urban horizon angles and the obstruction sky view (Ratti et al., 2005). The urban horizon angle (UHA) is the average angle of elevation of surrounding buildings from the centre of a given facade. Affected by the height of and distance between structures, the UHA accounts for building shading and significantly affects solar access, particularly in winter when the sun is lower in the sky (Baker and Steemers, 2000). High UHAs have the potential to shift heating and cooling energy demands in buildings by reducing solar gain through glazing while heat loss remains constant (Steemers, 2003; Baker and Steemers, 2000). Lighting energy demands are also affected by this factor (Steemers, 2003). Tables prepared by Steemers (2003) illustrate that the UHA can affect heating energy demand by as much as 30%, cooling demand by 20% and lighting demand by 150%, with greatest impacts occurring for south facing facades. For Steemers’ work, UHAs less than 15 degrees are considered to have a negligible effect on energy demand. For non-residential buildings, increasing the UHA by 10 degrees can result in a 10% increase in energy consumption, with greater impacts occurring for passive solar designs (Steemers, 2003). The calculation of the UHA can be relatively straightforward when dealing with uniform spacing and building heights (i.e. tan(UHA)=h/w). However, the calculation is complicated by more complex urban geometry. To account for variations and building heights and spacing in an actual urban context, Ratti et al. (2005) apply an algorithm using a weighted average of the UHA perpendicular to the facade and 6 UHAs within the range of 67.5 degrees to either side, at 22.5 degree intervals. Figure 2.1: Relationship between height (h) to width (w) ratio, urban horizon angle (u) and sky view factor (Vs), adapted from Robinson, 2006 Vs u w h 20 A review of the literature also illustrates that beyond UHA, two other main approaches are used to quantify obstructions adjacent to a given building facade: height to width ratios (Shashusa- Bar and Hoffman, 2003) and sky view factors (Cheng et al., 2006; Compagnon, 2004). Height to width ratios are simply the ratio of the height of an obstruction above a given facade reference point and the distance between the obstruction and facade (Robinson, 2006). Sky view factor is a measure of openness for a given surface, represented as a value between 0 and 1, where 1 means an unobstructed view of the sky and 0 means a completely obstructed view (Cheng et al., 2006). While these approaches and UHA each use different methods of calculation, they are all related quantities, as illustrated in Figure 2.1. Each measure, in essence, represents the same qualities of urban morphology: the distances between buildings and their respective heights, resulting in the obstruction of solar radiation and daylight. Cheng et al. (2006) illustrate an application utilising sky view factors, finding that the sky view factor is highly affected by FAR and site coverage (both measures of density) as well as building arrangement within the site (e.g. regularized versus random layouts). Changes in the sky view factor (between 0.06 and 0.50) resulted in variations in available daylight as great as 66%. The obstruction sky view, as defined by Ratti et al. (2005), is essentially the same as UHA, but calculated for adjacent, obstructing facades. Solar energy falling on a facade comes from two distinct sources: direct radiation from the sky and indirect radiation reflected by other surfaces, including obstructions. The obstruction sky view accounts for the amount of radiation reflected onto a given facade (Ratti et al., 2005). Steemers (2003) notes that highly compact urban structures may negatively affect the feasibility of passive heating or building integrated renewable energy production due to shading and other solar access limitations.  More dispersed development has greater potential for solar energy, daylighting and natural ventilation; however, the counter-productive effects of increased density can be avoided if UHAs (which limit access to sunlight and daylight) are kept under 30 degrees – typical for densities of approximately 2.5 FAR or 200 units per hectare (Baker and Steemers, 2000). Building morphology and urban structure can also work together to play a significant role in enhancing solar access on an urban scale. Knowles reviews how particular building shapes, such as stepped building facades and appropriately angled roofs can virtually eliminate issues of overshadowing on adjacent parcels (2003). Cheng et al. (2006) find that rearranging building layouts in a theoretical neighbourhood in more random configurations (i.e. not aligned in a grid), as well as randomly varying building heights, substantially improve solar access without reducing the amount of building floor area. This implies that relatively high development densities can be achieved without negatively impacting energy performance. 21 Further, vertical and horizontal surfaces (i.e. walls and roofs) face different considerations for solar access. Roofs – and horizontal surfaces in particular – are less affected by surrounding urban structure than vertical surfaces (Compagnon, 2004). Higher elevation surfaces of any orientation are also less subject to solar obstruction (Carneiro et al., 2008). The relative importance of horizontal and vertical surfaces for solar access may depend on design intent. For example, high site coverage development, which typically limits vertical solar access, is advantageous for photovoltaics due to the high amount of horizontal roof surface provided (Carneiro et al., 2008; Cheng et al., 2006). Compagnon (2004) finds that total solar irradiation in a study neighbourhood (including vertical surfaces) can be three times that of irradiation on roofs alone per square meter of floor area, emphasising that vertical surfaces (i.e. building facades) are an important factor in solar energy collection. Urban structure also relates to building energy through the urban heat island (UHI) effect.  As discussed in Ewing and Rong (2008), the UHI effect has several causes, including the proliferation of built surfaces which retain the sun’s radiation, the displacement of vegetation and related cooling and shading effects and the concentration of heat producing activities, such as vehicle travel. Urban structure characteristics associated with this effect include the density of development and spacing of buildings, which can concentrate the amount of heat-absorbing surfaces as well as heat- generating activities contributing to the UHI. The UHI effect can increase temperatures in typical urban areas by 1-3° C, with larger, more compact cities experiencing greater effects.  As expected, this effect serves, on average, to increase the need for cooling in the summer and decrease the need for heating in the winter; however, Ewing and Rong find that for most locations in the United States, the UHI effect provides a net reduction in energy consumption due to savings in heat energy and conclude that everywhere the overall positive effects of compact development far outweigh any negative effects of heat islands. More compact development further minimises the energy required for infrastructure and related maintenance and creates opportunities for district energy and heat sharing technologies that require concentrated energy demand (Steemers, 2003). Compact development and localised energy systems can also reduce energy transmission and distribution losses. For example, these losses account for roughly 7% of generated electricity in the United States (Ewing and Rong, 2008). 2�2 Urban form research Methods The attempt to describe and define urban form has been ongoing for centuries, across many disciplines, without arriving at a conclusive approach (Jabareen, 2006; Levy, 1999; Marshall, 2005a). Cities are alternatively thought of as constructed spaces, arrangements of parts, products of linked 22 decisions, patterns of activities or relationships, social or economic constructs, ecosystems or flows of information, among other conceptions (e.g. Kropf, 1993; Lynch, 1981; Marshall, 2005c; Jenks and Dempsey, 2007; Levy, 1999; Marshall, 2009; Anderson et al., 1996). Because cities influence and interact with so many natural and human phenomena, the bounding of urban form for examination or description is difficult (Lynch, 1981) and influenced by values, intentions and interests that are sometimes unexamined or unrecognized (Lynch, 1981; Levy, 1999). Additionally, the large numbers of actors and elements inherent to urban systems demand research approaches capable of simplifying and organising vast amounts of data for particular purposes. 2.2.1 Urban patterns Despite great diversity in the characteristics of cities, most urban forms have some basic features in common (Kostof, 1991; Marshall, 2009; Lynch, 1981; Anderson et al., 1996). The study of urban patterns builds on these commonalities to create abstract types that allow for generalisation across multiple, specific cases (Marshall, 2005c). In the most general terms, a pattern is any kind of recurring structure or feature (Marshall, 2005c). As the understanding of the parts is a prerequisite for understanding the whole (Lynch, 1981), an urban pattern is most often defined as a relatively small number of parts or elements (e.g. streets, parcels, buildings and open spaces) which are linked by a consistent, repeated set of systematic relationships (Habraken, 2000; Jabareen, 2006; Marshall, 2009; Miller and Cavens, 2008). These relationships tend to describe qualities such as connectivity and orientation, rather than metric data such as actual distances and areas (Marshall, 2005c) and aim to extract useful and meaningful information for a given purpose. Relationships imply similarity and continuity, rather than standardisation (Habraken, 2000). The understanding and measurement of the urban environment is necessarily spatial.  Urban form has been defined by many as consisting of spatial patterns or attributes (Jabareen, 2006; Lynch, 1981; Anderson et al., 1996; Mindali et al., 2004; Marshall, 2005c).  These spatial patterns are defined primarily by the arrangement of permanent objects – land forms, streets, buildings and infrastructure (Jabareen, 2006; Lynch, 1981; Anderson et al., 1996; Miller and Cavens, 2008) within which urban functions (transportation, exchange, interactions) occur (Lynch, 1981). Spatial models, therefore, depend on the location of urban elements and activities, “such that if locations change, results change” (Maguire et al., 2005). Numerous disciplines use spatial models to study various aspects of urban forms and systems: landscape ecology and resource conservation, economics (such as urban growth and change), demographics (including census data), transportation planning, community energy planning and others (Albeverio et al., 2007; Clifton et al., 2008). 23 Table 2.4: Summary of issues involved in the description and analysis of urban form Classification    The classification of urban form involves making distinctions based on specific attributes.  However, in reality, urban form is a morphological continuum (Marshall, 2005a) without  distinct or obvious boundaries.  Many urban conditions are found to be irregular or hybrids  of multiple patterns, unable to fit neatly into generic categories. Attempting to classify all  forms under these conditions runs the risk of ending up with an “arbitrary list” (Marshall,  2005a). Many researchers have found is that there is no set of clear, absolute pattern types  (e.g. Marshall, 2005c; Kostof and Castillo, 1992). Rather, classification depends on the  intended application, and must seek to balance between too few broad categories and too  many narrow categories for the intended purpose (Marshall, 2005c).  Analysis    The methods used to study urban form vary widely across several disciplines including  geography, urban planning, history, landscape planning, ecology and others. Marshall  (2005c) notes that there is no single, consistent method of specifying urban patterns across  the disciplines interested in urban form. Patterns may be differentiated based on one or  many characteristics (Lynch, 1981; Marshall, 2005a). They may be assessed using  qualitative methods (e.g. Marshall, 2005c), quantitative methods (e.g. Pont and Haupt,  2005), or a combination of approaches (Marshall, 2005a).   Scale and  resolution    The reviews of urban patterns presented by Marshall (2005a) and Lynch (1981) illustrate  the range of scales at which urban form has been examined. Lynch provides examples of  patterns at the city scale, representing overall shapes and configurations of entire urban  areas (e.g. linear or satellite cities), as well as finer‐grained patterns representing detailed  urban characteristics such as housing types (e.g. tower in the green) and classes of open  space (e.g. playgrounds and playfields). Marshall (2005c) notes that as interest in  sustainability research increases, the resolution of sustainability indicators is not being  matched by an equally high‐resolution understanding of urban form.   Representation    Due to high levels of variation in scale, classification and methods of analysis, urban form is  rarely represented consistently. Beyond the language used to discuss form and patterns,  patterns are also represented graphically and quantitatively in a variety of ways. Lynch  (1981) notes that 2D maps and diagrams are frequently used to represent urban patterns  but lack essential 3D information. The need for enhanced 3D representation of urban form  has been noted by many (Sheppard, 2005; Appleton and Lovett, 2003; Batty et al., 2000).  Further, Lynch notes the challenge of representing temporal aspects of urban form,  including evolution over time, as well as the challenge of representing patterns with  appropriate graphic detail to highlight significant characteristics (Lynch, 1981).   Application    The study of urban form can be applied in a variety of ways (Marshall, 2005c). In many  applications, patterns are used to interpret and understand existing or potential future  urban forms. Patterns may also be used to assist in the generation and description of  alternative design options (e.g. Criterion Planners, 2008) and associated policy. One  example of policy developed based on patterns is form‐based codes, such as SmartCode  (Duany Plater‐Zyberk and Company, 2008). Recognizing that urban form is evolutionary,  Anderson (1996) argues that the usefulness of archetypes may be limited by their static  nature; however, Marshall (2005c) notes that static representations of form are important  to decision makers, who experience urban form as static while making day to day  decisions. Importantly, patterns applied in the development of planning options and policy  should be recognisable to policy makers and related to local conditions (Marshall, 2005c).     24 Approaches to the description and analysis of urban form through the use of patterns have been methodologically diverse, encompassing a range of both qualitative and quantitative classification systems and varied forms of analysis and representation (Table 2.4). The following sections review some of the primary precedents and directions in pattern-based approaches to urban analysis, including more recent applications of computer-based modeling. 2.2.2 Qualitative pattern approaches The use of morphology, typology and urban pattern classification through history has largely been a qualitative endeavour. Scholars have carefully observed and studied urban conditions and described, categorised and interpreted urban elements and relationships in a variety of ways. Qualitative pattern approaches, as defined for this paper, are any methods of defining urban patterns based on non-quantified qualities or characteristics, such as classifications by pattern shape (e.g. linear, nodal), by the presence of particular urban elements (e.g. services, cul-de-sacs) or by organisational properties (clustered, open plan). Some qualitative approaches also attempt to address less tangible aspects of urban form, such as use, meaning or social and historical contexts. Four examples of qualitative approaches are presented in this section. Kostof, in his last two books, approaches urban history through the examination of urban patterns and urban form elements. For Kostof, there are two types of urban forms – organic forms that develop over the passage of time without design and planned forms, such as grids, which are intentionally created (Kostof, 1991). He notes that these forms are not exclusive, often existing side by side. Moreover, there are parts of each in the other: irregularities of building shape and placement often occur along planned, regularised streets, and ordered patterns often arise from spontaneous development. Kostof also examines urban patterns in elevation, noting the function of urban skylines as a calculated and cumulative process, creating urban “signatures” (Kostof, 1991). All settlement patterns are comprised of four common elements: a changing edge, internal divisions, public places and streets (Kostof and Castillo, 1992), and all elements are intertwined with social and historical context. Kostof’s work focuses more on defining these principles and elements across history than on identifying particular urban patterns; he argues that “cities are too particular a phenomenon, specific to moments in time and to vicissitudes of site and culture, to be pinned down by absolute taxonomies” (Kostof and Castillo, 1992). Lynch (1960) similarly notes that urban form is difficult to define in terms of concrete patterns (see also Lynch, 1981). He hypothesizes that the moving elements of a city, and people in particular, are as important a consideration as the static, physical elements. His classifications of urban form are consequently based on how urban form is perceived by inhabitants (Lynch, 1960). Through studies 25 conducted in three major cities, Lynch concludes that an individual’s understanding of urban form, or “environmental image,” is based not only on the spatial pattern of urban elements, but also on the ability to identify and associate meaning with particular elements (Lynch, 1960). He then identifies key elements present in individuals’ environmental images: paths; edges; districts; nodes; and landmarks. While these classifications are not “morphological” (Hall, 1997), they are closely tied to urban form and physical patterns. In another qualitative approach to the classification of patterns, Marshall (2005c) considers a combination of morphological and other characteristics in defining a set of five patterns of local urban structure, for which seven criteria – each with two to three attributes – were evaluated. Selected criteria include land use, the presence of services and the relationship to transportation spines, among others. What the research finds is that the five local structures can be differentiated with only two criteria: the relationship of the local centre to the neighbourhood and the location of the neighbourhood along the transportation spine. However, the five local patterns of urban structure presented by Marshall are distinctly suburban in nature, highly generalized in characteristics (for example, the description of land use is limited to single-use or mixed), and targeted towards discussions on new development. Steadman (2000) uses a qualitative classification of building morphology as a tool for subsequent energy analysis in the UK. For the project, built forms are described parametrically, rather than dimensionally, enabling a single dimensionless form to stand for a large number of real examples. For example, a simple building shape would be a “shed,” with a rectangular plan and a double- pitched roof with gables, regardless of specific dimensions. The forms are then classified according to two main characteristics identified as important for energy analysis: the “texture” of the internal plan (e.g. open or cellular) and whether spaces are artificially or naturally lit. 2.2.3 Quantitative pattern approaches Less frequently, studies of urban form and patterns have taken a quantitative approach. Pont and Haupt (2005) note that “a quantitative analysis of the built form has not been applied thoroughly.” However, there is a growing recognition that quantitative descriptions and analysis of built form can serve to expand the power and usefulness of typically qualitative approaches (Pont and Haupt, 2005; Marshall, 2005a; Lynch, 1981). Additionally, the increasing power of digital information and analysis tools is providing new opportunities for detailed quantitative studies of urban form. Several approaches to pattern classification using digital tools are described in Section 2.2.4. 26 In his approach to street pattern classification, Marshall (2005a) argues that “capturing the character of... patterns implies the ability to handle heterogeneity” and suggests that heterogeneity requires the use of quantitatively defined properties. To classify street patterns, he proposes three quantifications: T and X ratios; cell and cul-de-sac ratios (all defined in the book); and the plotting of these numbers to create a 2-dimensional space. By graphing these numbers, Marshall creates a spectrum of street patterns shown in relation to one another, allowing for greater specificity of characteristics. In further work published the same year, Marshall (2005b) uses the quantification of street patterns according to continuity, connectivity and depth measurements to determine the relative probability that a particular pattern will occur. Pont and Haupt (2005; 2007) have likewise attempted to link spatial patterns to quantitative data. They note that the typical use of density as an identifying concept of built form and urban pattern is problematic. An analysis of three case studies illustrates that neither of the two commonly used quantitative metrics of density (units per hectare and FAR), adequately identify similar urban forms or differentiate between unlike forms. Pont and Haupt redefine density using four quantitative metrics: FAR; ground space index (i.e. building coverage); open space ratio (ratio of open land to building floor area); and layers (average number of building storeys). By graphic these metrics simultaneously, a more consistent classification of urban forms is achieved. For classification of density at the neighbourhood and larger scales, a fifth metric, network density (length of street network per hectare), is also considered to differentiate varying block scales (2005). The authors further suggest that the urban patterns defined through the quantification of density can be used as a framework to evaluate pattern performance on a variety of issues, such as amount of open space, building shadowing, parking and energy consumption, although specific methods have not yet been developed. Researchers at the University of British Columbia (Miller and Cavens, 2008) have used the statistical analysis of existing urban form (land use, building type and population) to discern between urban patterns. In recent work, a city was divided into zones representing similar patterns of development. For each zone, land use data was compared and zones with statistically similar land uses clustered to generate “average” patterns. Work currently in progress is using further quantitative analysis to automate the assignment of previously generated patterns to regional scale areas. 2.2.4 Patterns in computer modeling applications One of the emerging approaches in both academic and applied urban research is the use of urban patterns to simplify modeling data while capturing key urban form and related properties. The concept of classifying and describing urban form through the definition of patterns has 27 been discussed in previous sections, and has a long history prior to incorporation in modeling applications. As discussed, most urban forms have some basic features in common, and the study of urban patterns builds on these commonalities to create abstract types that allow for generalisation across multiple, specific cases. The incorporation of patterns in modeling has had, and is having, several important implications in urban form research and urban planning. Some studies are using pattern-based concepts to automate the classification of urban areas or building stock (e.g. Steiniger et al., 2008). Others are using patterns to approximate existing urban conditions and to model future scenarios at multiple scales. Depending on the amount and type of data incorporated in the patterns, analyses of sustainability indicators, including energy and GHG emissions can then be completed (e.g. Criterion Planners, 2005). Patterns are also being included as information and engagement tools in public planning processes, allowing stakeholders to participate in the creation of patterns or translate patterns into key policy decisions. Researchers at the University of British Columbia are conducting research on how patterns may be used for new approaches to transportation modeling. Urban pattern modeling approaches vary greatly depending on the particular scale of the object of study (e.g. urban structure, land use, street pattern, or building type).The variety of possibilities for classifying urban patterns has led to great inconsistency in their use (Marshall, 2005a; Marshall, 2005c), which is also evident in the variety of methods used in classification efforts. This section focuses on the description of several modeling methods used in classification and pattern-making processes. A first set of methods used in the modeling of patterns involves classification through cluster analysis, where multi-criteria datasets are analysed to find groups possessing similar characteristics (Singleton, 2007; Jones et al., 2001). Two examples illustrate how cluster analysis is used in different ways and at different scales to create classifications suited to particular purposes in urban research. In the first example, Jones et al. (2001) use a clustering model to classify houses in the UK according to estimated energy performance and energy retrofit potential. Houses were classified as one of five age groups and one of 20 built form types, resulting in a total of 100 possible classifications. The built form types were defined according to four selected characteristics believed to have the greatest impact on residential energy performance: heated ground floor area; façade; window to wall ratio; and exposed end area. Each of the 100 house types was modeled using the Energy and Environmental Protection (EEP) model to determine GHG emissions, energy ratings and annual energy costs, a process that would have been prohibitive to carry out for each individual building. 28 In a second example, Steiniger et al. (2008) attempted to automate the definition of patterns of urban structure in Switzerland and the UK according to five classifications: industrial and commercial areas; inner city; dense urban areas; disperse suburban areas; and rural areas. For this process, a geographic information system was used to describe urban structures according to five morphological characteristics: built-up area density; building size; building shape; squareness of building walls; and building orientation. These data were analysed using a series of algorithms in order to assign areas to a final structural type. What the project found, however, is that morphological characteristics do not consistently classify urban structures, as the characteristics were often not distinct between urban structure types. Further, the method relies on homogeneity of characteristics within a given urban pattern, and could not account for the more gradual transitions between patterns, such as those occurring between rural and suburban areas. Marshall (2005a) applies similar analysis techniques to the classification of street pattern types according to a more finely delineated spectrum. He suggests that “capturing the character of real street patterns implies the ability to handle heterogeneity” and that heterogeneity requires the use of quantitatively defined properties. To classify street patterns, he proposes three quantifications: T and X ratios; cell and cul-de-sac ratios (defined in his book); and the plotting of these numbers to create a 2-dimensional space. By graphing these numbers, Marshall creates a spectrum of street patterns shown in relation to one another, allowing for greater specificity as well as clustering of characteristics. Pont and Haupt (2005; 2007) similarly graph urban patterns according to five quantifications of urban density: FAR; ground space index (i.e. building coverage); open space ratio (ratio of open land to building floor area); layers (average number of building storeys); and network density (length of network per hectare). While the above urban form modeling methods all deal with pattern classification and modeling based on quantified urban form attributes, another approach models patterns based on the assembly of smaller scale classifications or types (such as parcels, buildings or land uses) to create larger scale patterns of development. This approach to urban pattern modeling typically uses spatial and quantitative municipal data, such as land use and residential unit counts, rather than morphological data, to determine existing patterns according to density and/or land use mix. Because these approaches are more closely aligned with how planning decisions are made (e.g. parcels, streets and open spaces grouped by zoning designations, development plans, etc.) many of these modeling methods have evolved in applied research in planning and urban design (e.g., Miller and Cavens, 2008), or in the private sector (see for example Criterion Planners, 2005; Fregonese Associates et al., 2009). When applied to real planning processes, “future” patterns are often collaboratively developed by experts and stakeholders for the purpose of plan or scenario creation. 29 2�3 building energy Modeling Methods The modeling of building energy consumption occurs at several scales, each with varying methods and tools. Work to date on modeling the building energy implications of urban form appears to be relatively well developed at the building scale, where energy simulation models have been refined for decades (Crawley et al., 2005), and also at the regional scale, where aggregate estimations of energy are able to provide sufficient information for high-level policy questions (Johnson et al., 2005). The neighbourhood-scale impacts of urban form on energy, particularly the cumulative effects of relationships between multiple buildings (e.g. building massing, heights, arrangements and spacing) are less frequently considered and have less developed methods available for modeling. However, increasingly sophisticated energy simulation tools, including solar radiation simulation tools such as Radiance (Cheng et al., 2006) and building energy meta-tools such as VE-Pro (see Chapter 3) now have the capacity to model the performance and interactions between multiple buildings. Platforms for spatially explicit data storage and analysis, such as GIS, CityGML and others likewise offer opportunities for enhanced neighbourhood-scale energy analysis. At the same time, approaches such as the lighting and thermal (LT) method (Baker and Steemers, 2000) and energy-specific design indicators (Kellett, 2009) offer means by which to compare simulated energy performance results according to quantified urban form variables. A majority of factors affecting building energy consumption relate to building design (i.e. building morphology and envelope characteristics) and building systems (Mitchell, 2005; Ratti et al., 2005; Baker and Steemers, 2000). Depending on the scale of the model and the number of buildings to be considered, these factors may be assessed for individual buildings (e.g. building energy simulations), or broad assumptions may be applied based on averages and related analysis to simplify complexity (e.g. Environmental Change Institute, 2005; Miller and Cavens, 2008). For the purposes of this paper, models are categorized into three scales – individual building energy models, neighbourhood energy models and regional/national energy models (Table 2.5). Table 2.5: Typical energy modeling approaches at different scales Building Scale Neighbourhood Scale Regional Scale Building Morphology Individually modeled Individually modeled OR  Assumed Assumed Building Envelope Individually modeled Assumed Assumed Building Systems Individually modeled Assumed Assumed 30 Beyond the individual building scale, for which numerous software packages are available, the assessment of multiple buildings at the neighbourhood and larger scales generally takes one of two approaches (Mitchell, 2005): 1) Building activity descriptions (e.g. residential, office, industrial), multiplied by energy coefficients 2) Geometric descriptions (e.g. LT method, Section 2.3.3) Based on current levels of data availability at various scales, Mitchell (2005) finds that the activity- based method is most appropriate at the municipal, regional and national scales, while geometric methods are most appropriate at the neighborhood scale. However, in application, crossover between methods and scales does occur. This section will focus on neighbourhood-scale building energy modeling, addressing both approaches described by Mitchell. The section begins with a brief discussion of the other two scales for context. 2�3�1 building-scale energy models Modeling building energy consumption at the scale of individual buildings has been a field of study for over 50 years (Crawley et al., 2005). Hundreds of models have been developed over this time, designed for different purposes, audiences and levels of complexity (Crawley et al., 2005; Jacobs and Henderson, 2002). The spectrum of available tools ranges from highly technical, engineering tools aimed at simulating particular aspects of energy performance (e.g. detailed HVAC performance) to simplified tools for building designers that assist with common design questions (e.g. equipment Table 2.6: Categories of building energy tools with examples, adapted from Jacobs and Henderson, 2002 Tool Type Functions Examples Practitioner Design Tools Automation of common tasks associated  with typical design process Solar‐2 (shading); GS2000 (geothermal  sizing); EnergyGuage (home energy rating) Whole Building Energy Analysis  Tools Detailed prediction of annual energy use  and operating costs EnergyPlus; DOE‐2 (calculation engines);   eQUEST; Energy‐10 (hourly simulation  tools) Energy and Environmental  Screening Tools Simplified analysis of economic and  environmental impacts of selected  technologies RETScreen (renewable energy); Athena  Impact Estimator (life‐cycle impacts); BLCC  (life‐cycle costing) Specialised Analysis Tools Technically accurate, detailed   simulations of building or system  performance  Radiance (solar/daylighting); THERM (heat  and moisture transfer); Flovent (fluid  dynamics) 31 sizing). Between these extremes are modeling tools that combine detailed simulations with user- friendly interfaces to support a wider range of users (Jacobs and Henderson, 2002). Jacobs and Henderson (2002), along with Crawley et al. (2005) provide highly detailed reviews of available tools, calculation engines and their respective features (Table 2.6). A majority of building energy simulation tools require detailed data on building construction and systems design to determine energy performance.  Common inputs to such models include: building geometry; construction materials; heating and cooling systems; occupancy schedules; and weather data. Some tools also take into account additional external site factors, including building orientation, site terrain, ground contact conditions and external shading from vegetation and nearby buildings (Cavens, 2007). Typical outputs from such models include energy use by time step and end use (e.g. heating, hot water, or lighting). More recently, building energy modeling tools have been linked to or integrated with 3D building modeling programs, enabling 3D models created for design purposes to act as databases of building geometry, materials and systems for use in energy analysis (Jacobs and Henderson, 2002). Software packages such as VE-Pro and EnergyPlus are now able to import architectural models built in SketchUp and similar programs for energy modeling (http://sketchup.google.com/green/analysis. html), and architectural software suites such as Autodesk have included environmental and energy analysis capabilities in select packages. 2.3.2 Regional and national-scale energy models Large scale energy use models such as the United Kingdom’s Domestic Carbon Model (UKDCM) (Environmental Change Institute, 2005), the Energy and Environment Prediction Model (EEP) (Ratti et al., 2005; Gadsden et al., 2005; Jones et al., 2001) or the CIMS model used within Canada (MK Jaccard and Associates Inc., 2008) use statistical descriptions of building stock and changes to the building stock (such as retrofits, system upgrades and new construction) to estimate aggregate energy impacts at the city, regional or national scale (Environmental Change Institute, 2005; Mitchell, 2005).  These models calculate energy using activity-based energy coefficients (e.g. GJ/ m2/yr) and are typically not spatially explicit at the local level (Mitchell, 2005). They do not include form-based factors such as overshadowing or building morphology (Ratti et al., 2005) due to the large number of buildings being accounted for. For example, the national-scale energy model developed by Johnson et al. (2005) uses only two dwelling types (pre- and post-1986) to account for the UK housing stock. The authors assert that, at the national scale, “the impact of dwelling type on energy use and CO2 emissions is small, in comparison with the impact of the thermal characteristics of the building fabric and system efficiencies.” 32 Inventories of energy and GHG emissions (for example the Community Energy and Emission Inventories developed by the Province of British Columbia), are also based on aggregations that exclude spatial or form-based data. Instead such inventories use either aggregated energy consumption data provided by energy utilities or building activity data paired with average energy consumption coefficients (The Sheltair Group, 2007). Some regional and municipal scale modeling efforts have attempted to spatialise data and make links to generalized urban form characteristics, such as density. Projects including the Vulcan Project (http://www.purdue.edu/eas/carbon/vulcan/index.php) and San Francisco’s Urban EcoMap (http:// urbanecomap.org/) have spatialised greenhouse gas emissions by census tract, and compared these numbers to population density. For the City of North Vancouver’s 100 Year Sustainability Vision (Miller and Cavens, 2008), researchers mapped greenhouse gas emissions according to pattern of development, including data on land use mix and housing density. Criterion Planners also maps estimated GHG emissions spatially using their developed “Cool Spots” methodology, based on combinations of urban form variables including density, co-location of services and transit and network connectivity (Criterion Planners, 2008). 2�3�3 neighbourhood-scale energy models Modeling building energy use at the neighbourhood scale allows for greater consideration of urban form and pattern than either building or city/regional scale modeling; however, few models or methods at this scale have been developed (Ratti et al., 2005). A review of neighbourhood-scale modeling literature reveals that many models explicitly dealing with urban form remain largely in academic or theoretical contexts, while applied modeling at the neighbourhood scale tends to utilize building archetypes and energy coefficients, similar to regional scale modeling approaches. Both of these neighbourhood-scale modeling approaches will be discussed in this section. 2.3.3.1 Form-based models The study of relationships between building energy and urban form have taken many approaches over several decades (e.g. March, 1972; Owens, 1986). However, a number of recent approaches to modeling the energy implications of urban form are based on the Lighting and Thermal (LT) method, developed by Bakers and Steemers (2000). The LT method is based on the concept of quantifying areas of passive and non-passive zones within study area buildings as a means of estimating the relative energy performance of varying urban forms. Passive areas are those located within approximately six meters of a building’s perimeter, providing the potential for access to daylight, solar gains and natural ventilation. The LT modeling method supports the manipulation of only a few key form-related design variables, using average assumptions for detailed building and system 33 design. Energy performance is estimated using a set of pre-modeled “LT curves,” representing annual primary energy consumption per square meter for each of the cardinal orientations based on a simple, archetypal, non-domestic building. The LT method is not intended to provide detailed, accurate predictions of energy consumption, but can be used as a means of comparison between design options (Baker and Steemers, 2000). At the neighbourhood scale, the LT method provides a simple way to compare different patterns of urban form through the quantification of passive areas, without requiring detailed knowledge of individual building characteristics. A 2005 study applies the LT method to an analysis of urban patterns in Europe (Ratti et al., 2005). The researchers use the same LT model, but include calculations for urban horizon angles and obstruction sky view to account for relationships between adjacent buildings, such as shading. To further facilitate the collection of the required morphologic and geometric data required for the LT method, digital elevation models (DEM) were prepared of each study area, and image processing techniques were used to derive the necessary quantifications. The study finds a 10% difference in energy consumption due to morphological effects and concludes that the ratio of passive to non-passive areas is a better predictor of energy consumption than surface to volume ratios for non-domestic buildings in the London climate. While the original LT method was developed for non-domestic buildings in the UK (Baker and Steemers, 2000), the general concept of using passive and non-passive areas as a proxy for relative energy performance has since been applied to a variety of studies, including both general energy studies of urban form (see for example Mitchell, 2005; Salat and Morterol, 2008) as well as issue- specific studies, such as solar access (see for example Ratti et al., 2005; Cheng et al, 2006; Gadsden et al., 2005). Modeling studies linking building energy and urban form have focused not only on building energy consumption, but also on renewable energy generation potential. Studies of urban form and solar energy access have been a particular focus, utilizing a variety of methods. Many of these studies model the solar radiation able to be captured on building surfaces, taking into account orientation, climate and building shading. Urban form is represented by either 3-dimensional (see for example Mardaljevic and Rylatt, 2003) or DEM models (see for example Carneiro et al., 2008). Irradiance is calculated using one of a number of estimation and simulation methods, ranging from relatively simple shadow analysis (see for example Carneiro et al., 2008) to advanced ray tracing techniques (see for example Compagnon, 2004). 34 Robinson (2006) cautions that solar analysis based only on morphological indicators (e.g. sky view factor, urban horizon angle and height to width ratios) are not able to adequately reflect the actual solar potential of local urban conditions. However, in a comparison of morphological analysis to complex solar radiation simulations, Robinson noted that morphological analyses using aggregate measures of morphology are well correlated to the more accurate simulated results and so may still provide useful analytical information. These conclusions reflect the assertions of Baker and Steemers that morphological analysis will not predict actual performance, but can be useful for explanatory and comparative purposes. The use of morphological analysis may be particularly useful as a complement to simulated results, which consider multiple factors in integrated ways and may therefore obscure the impacts of individual urban form characteristics. 2.3.3.2 Archetype-based models In applied research projects, neighbourhood-scale energy modeling more commonly relies on building archetypes and energy coefficients – for example, energy consumption per square meter (Canadian Urban Institute, 2008; Criterion Planners, 2008), per residential unit (Miller and Cavens, 2008) or per building (Fregonese Associates et al., 2009) by type. Energy coefficients may also represent a range or tiers of performance standards, for example 25% and 50% better than the Model National Energy Code (Canadian Urban Institute, 2008; Fregonese Associates et al., 2009). In one reviewed project, general assumptions about building orientation were also included (Fregonese Associates et al., 2009). These methods are able to more quickly generate baseline and future urban form scenarios for the evaluation of neighbourhood-scale planning decisions through simple, aggregate calculations, but do not generally address more complex urban form interactions such as shading. In comparison to city and regional scale models using a building archetype and energy coefficient approach, neighbourhood-scale models using this method tend to include a finer- grained, more diverse variety of building archetypes, in order to at least partially account for urban form variations (e.g. morphology). 2�4 conclusion The preceding review describes a range of tools, approaches and lessons for exploring the relationships between urban form and building energy at a variety of scales. The following chapters build from the lessons found in the literature, using spatial, 3D models of urban form and building energy simulation tools to further describe the impacts of urban form decisions on building energy at the neighbourhood scale. 35 cHAPter 3: MetHoDs As discussed in Chapter 1, the intent of this research is to provide a more comprehensive understanding of the relationships between urban form and building energy to better inform urban planning and development decisions. Towards that end, the project examines a series of identified urban form characteristics including building shape, glazing, orientation and urban structure (i.e. building spacing and arrangement) for their impacts on building energy demand and local energy generation potential. Extending from the literature described in Chapter 2, the research uses 3D and building energy simulation modeling tools to quantify building energy impacts of and interactions between urban form characteristics at three scales: individual building archetype (BA); building archetype with local shading (LS); and urban pattern (UP). Examining urban form and building energy at multiple scales enables a more comprehensive analysis of urban form effects on building energy. Consistent methods of 3D and energy simulation modeling are applied across all three scales of urban form. This chapter presents the broad methodological approach for the project. Section 3.1 identifies how the approach to this research fills important methodological gaps existing in the current literature. Section 3.2 defines the three scales of urban form addressed and details the particular urban form characteristics considered at each scale. Section 3.3 reviews the building energy simulation (BES) tool used for the research and identifies the key assumptions made for energy simulation modeling. Section 3.5 describes how the urban form (i.e. spatial) and energy simulation data are brought together in the analysis of results, and Section 3.6 reviews the limitations of the research approach. Additional details regarding how the methods were applied to the three studies composing the thesis will be covered in subsequent chapters. 3�1 Methodological Gaps in current research The methodology developed for this project builds on the approaches of several previously conducted urban form and energy use studies. The research is particularly influenced by parametric building design research conducted for representative buildings (Baker and Steemers, 2000; Cobalt Engineering, 2009), a spatial energy study of existing European neighbourhoods (Ratti et al., 2005) and an abstract parametric study of urban density and spatial arrangement (Cheng et al., 2006). These studies isolate urban form effects on building energy by using standard assumptions for the numerous other factors outside of urban form that influence building energy, such as the thermal properties of building envelopes, system efficiencies and occupant behavior. Consequently, the methods used in this and the reference studies do not predict actual energy use, but are intended 36 to provide a means of comparison between urban form choices given standard conditions for other energy-related factors. The literature review provided in Chapter 2 identifies that building energy is influenced by urban form through building morphology and urban structure. Building morphology includes the size and shape of individual buildings, building orientation, and the amount and distribution of glazing on building facades. Urban structure includes the density, arrangement and spacing of buildings relative to one another, primarily influencing building shading and solar access. Literature reviewed for this project further identifies that building activity (e.g. commercial and residential uses) must also be considered in conjunction with urban form characteristics to fully describe impacts on building energy. The identified research precedents have made significant contributions to the understanding of urban form and building energy; however, none provides a complete analysis of urban form at the neighbourhood scale based on the urban form characteristics identified (Table 3.1). A notable difference between studies is the treatment of urban structure. One study (Cobalt Engineering, 2009) does not include the impacts of urban structure on building shading and solar access, modeling only individual buildings. Baker and Steemers (2000) consider the impacts of urban structure on an individual building, but do not address that building’s effect on adjacent buildings (i.e. unidirectional analysis) (Figure 3.1). Both Ratti et al. (2005) and Cheng et al. (2006) consider mutual interactions between a number of buildings within a given area (i.e. multi-directional analysis), but include only limited analysis of building morphology. This research, through the consideration of urban form at multiple scales, addresses a broader range of urban form characteristics than previous literature with the intent to provide more comprehensive information on the relationships between urban form and building energy. The thesis uses analysis at three scales to explore a full range of building morphology characteristics while also addressing the multi-directional effects of urban structure on building shading and solar access at scales larger than the individual building (Figure 3.2). In addition, building activity is included through sensitivity testing (Section 3.2) to determine how the effects of urban form on building energy vary as building activity changes. 37 shading eects shading eects building energy impacts building energy impacts building energy impacts Unidirectional analysis Multi-directional analysis Figure 3.1 Unidirectional and multi-directional analysis of urban structure Table 3.1: Urban form characteristics considered in selected building energy studies Study Building Form Glazing Orientation Urban Structureb Activitya  Cobalt Engineering, 2009 Y Y Y Baker and Steemers, 2000 Y Y Y U Y Ratti et al., 2005 Y Y Y M Cheng et al., 2006 M Miller PhD Thesis Y Y Y M Y aBuilding Morphology/Activity: Y = included in study bUrban Structure: U = unidirectional analysis; M = multi‐directional analysis Building Morphologya 38 3�2 research Approach To fully understand the relationships between energy and urban form, one must consider the means by which to describe and measure urban form, the means by which to describe and measure building energy and the means by which to discern relationships between the two. A general framework for this approach is described by Steinitz (1990) who presents six, iterative levels of design and planning inquiry. He summarizes his approach as follows: “To decide to make change (or not) [Level 6], one needs to know how to evaluate alternatives. To be able to evaluate alternatives [Level 5], one needs to know their comparative impacts from having simulated changes. To be able to simulate change [Level 4], one needs to know what changes to simulate. To be able to consider changes to test (if any), one needs to evaluate how well the current situation is performing [Level 3]. To be able to evaluate the situation, one needs to understand how it works [Level 2]. And in order to understand how it works, one needs representational schemata to describe its current state [Level 1].” figure 3�2 Inputs to and outputs of building energy analysis URBAN FORM CHARACTERISTICS (Building Morphology) • building compactness (i.e. S:V) • roof shape • glazing ratio • glazing distribution • building orientation CONSTANTS/ASSUMPTIONS • climate, topography, vegetation • internal gains (e.g. equipment, lighting, people) • mechanical systems • building material properties (e.g. reflectivity) • thermal resistance (e.g. insulation) • building operations (i.e. occupant behaviour) • building activities (i.e. use) URBAN FORM CHARACTERISTICS (Urban Structure) • building spacing (i.e. UHA) • street configuration • development density (e.g. FAR) • building types • building arrangement OUTPUTS • heating demand • cooling demand • district energy potential • solar energy potential EXCLUDED • natural ventilation • daylighting • electrical demand   (e.g. lighting, appliances) 39 Adapting Steinitz’s approach, the methodology used for this project is comprised of three key parts: 1) To understand and describe existing conditions and possible changes,  urban form characteristics at three scales (Section 3.2) were selected and modeled using 3D modeling software [Levels 1, 2 and 4], providing the quantitative and spatial urban form data for the project. 2) To evaluate the building energy performance of existing conditions and possible changes, each 3D urban form model was imported to and analysed in building energy simulation software (Section 3.3) [Levels 3 and 5], providing energy demand and consumption data. 3) Using the urban form data as independent variables and the energy performance data as dependent variables, sensitivity, regression and other analyses were completed to compare urban form characteristics and to draw conclusions regarding the relationships between urban form decisions and building energy (Section 3.4) [Levels 5 and 6]. In reality, these three parts of the research have been carried out iteratively throughout the project, but are presented sequentially here for clarity. Detailed descriptions of the research methodology are provided in the following sections. Figure 3.3 illustrates the research process and relationship to Steinitz’s levels. 3.3 Urban Form Characteristics at Three Scales The research precedents identified in this chapter examine the relationships between urban form characteristics and building energy at three distinct scales: Cobalt Engineering (2009) looks at individual building archetypes in isolation; Baker and Steemers (2000) consider individual buildings but include the shading effects of adjacent buildings; and both Ratti et al. (2005) and Cheng et al. (2006) assess the impacts on and interactions between multiple buildings at the neighbourhood (i.e. urban pattern) scale. However, none of these scales in isolation allow for the analysis of a full range of urban form characteristics. This project applies modeling and analysis methods at all three scales (Figure 3.4): individual building archetype (BA); building archetype with local shading (LS); and urban pattern (UP). Examining urban form and building energy at multiple scales enables a more comprehensive analysis of urban form effects on building energy. 40 12 building archetypes 6 existing urban patterns 8 new pattern variations Parametric studies URBAN FORM STUDIES ENERGY SIMULATIONS LEVELS OF INQUIRY 1 2 3 4 4 Representation of existing conditions Understanding of existing system Evaluation of existing system Development of alternatives Evaluation of alternatives Recommendations for change 1 2 3 4 5 6 5 6 Urban Pattern (UP) Local Shading (LS) Building Archetype (BA) Chapter 4: Existing urban form conditions Chapter 5: Building energy and urban structure Chapter 6: Urban form alternatives for building energy performance Energy simulation completed in IES VE-Pro figure 3�3: research process 41 As suggested by the three part approach to the research outlined above (Section 3.1), urban form characteristics are described and evaluated using two distinct modeling processes at each of the three scales: 1) 3D modeling – three dimensional models of individual building archetypes, local shading elements and urban patterns are constructed using SketchUp 3D modeling software (Section 3.2.5). 2) Building energy simulation (BES) – 3D models are imported to VE-Pro, a building energy simulation software package, for energy analysis (Section 3.3). Each of the three scales has distinct benefits and limitations for building energy analysis which are discussed in the sections below. figure 3�4: three scales of urban form analysis Building Archetype (BA) scale Local Shading (LS) scale Urban Pattern (UP) scale 42 3�3�1 Geographic context To bound the range of urban form characteristics (building morphology and urban structure) examined in the research project, considered urban form conditions were limited to those existing or under consideration within the Metro Vancouver region of British Columbia (Figure 3.5). This project does not attempt to provide analysis on all forms of development occurring within the region, but rather focuses on those residential and mixed-used patterns that comprise a majority of the region’s developed land. The project examines both existing urban form characteristics and variations on those characteristics which represent possible future conditions based on current directions and best practices in local planning. figure 3�5: Metro Vancouver region District of North Vancouver West Vancouver Lions Bay City of North Vancouver Bowen Island Vancouver Richmond Belcarra Port Moody Anmore Burnaby New Westminster Coquitlam Port Coquitlam Pitt Meadows Maple Ridge City of Langley Township of Langley White Rock Surrey Delta 43 3.3.1.1 Land use The Metro Vancouver region is located in the southwestern corner of mainland British Columbia, Canada. The region consists of 21 incorporated municipalities and one unincorporated area. It is the most densely populated region in British Columbia and includes 13 of the province’s 30 largest cities by population. By 2041, Metro Vancouver is projected to have a population of 3.4 million residents, an increase of 55% from 2006. Over the past several decades, the region has addressed high levels of growth with a range of measures including the Agricultural Land Reserve established in 1973 (Metro Vancouver, 2011b), the Liveable Region Strategic Plan (LRSP) adopted in 1996 (Greater Vancouver Regional District, 1999) and the recent Metro Vancouver Regional Growth Strategy (RGS), adopted in 2011 (Metro Vancouver, 2011a). The LRSP and RGS both promote concentrated growth in urban centres connected by high frequency transit. These policies have been successful in promoting compact growth, including: • From 1991 to 2003, 65% of the dwellings constructed in the region were multi-family units • From 1991 to 2001, the population in regional town centres grew by 41%, compared to 29% growth in the region as a whole • In 2001, 65% of the population resided in Growth Concentration Areas at a density of 32.5 people per hectare, compared to 13.4 people per hectare in the region as a whole • From 1991 to 2001, average population density in the region increased from 18 to 22 people per hectare (Greater Vancouver Regional District, 2005) Despite these successes, the region is still characterised by primarily low-density land uses, including a large proportion of single family residential development. A 2006 land use inventory of the region shows that commercial and residential land uses make up 15.5% of the region’s land base. Of this area, 64.5% is single family and duplex development (Metro Vancouver, 2008). While the region’s urban centres are relatively well-served by transit, many of these single family areas continue to be automobile dependent. Overall, 76% of trips in the region are still completed by private vehicles (Metro Vancouver, 2007). 3.3.1.2 Climate Metro Vancouver has a temperate, maritime climate. Weather in this climate is commonly overcast, with significant precipitation throughout much of the year. Metro Vancouver’s location in a maritime climatic region means that the area experiences cooler summers and warmer winters than other locations at similar latitudes (Whiting and Lai, 2008). For building insulation requirements, Metro Vancouver is categorised in the ASHRAE International Climate Zone (CZ) 5C, Cool-Marine, the 44 mildest climate zone designation in Canada. For this project, all energy simulations are run using weather data for the Metro Vancouver climate, using weather files available in the building energy simulation software. 3.3.2 Building archetype (BA) scale The analysis of individual buildings or building archetypes for building energy performance has a long and established history (Crawley et al., 2005). Examining variations in urban form characteristics at this scale enables the impacts of building morphology (i.e. building shape, glazing and orientation) on building energy to be isolated and quantified. However, in application, buildings are rarely isolated from the impacts of adjacent structures, such that consideration of the BA scale alone does not provide sufficient information to assess relationships between urban form and building energy. In reality, the building energy effects isolated at the BA scale will be altered by shading impacts from adjacent structures. In addition to isolated building morphology effects, BA scale analysis provides a baseline against which the energy performance of building archetypes situated within various local shading conditions can be compared. Building archetypes were developed to represent typical building morphology characteristics in the Metro Vancouver region. The approach to the identification and classification of building archetypes was adapted from the Elements of Neighbourhood database (elementsdb.sala.ubc.ca) developed by Kellett and Girling (2012) at the University of British Columbia. The database categorization approach uses a nested hierarchy of increasingly specific land use and urban form characteristics highly amenable to the description of building archetypes in energy analysis applications. The approach breaks out building archetypes first by land use (i.e. building activity), secondly by general building form (i.e. detached, attached, and stacked) and thirdly by more detailed morphological characteristics such as compactness and glazing. The selection of building archetypes for the project was undertaken with the intent to differentiate between key urban form attributes while still keeping the total number of archetypes as small as possible. For example, detached single family houses vary in form most substantially as parcel width varies. In Vancouver residential areas with gridded street configurations, parcels tend to be 10 meters (33 feet) wide, while in areas with tributary configurations, parcels are typically 15 meters (50 feet) wide or greater. These differences in parcel dimensions result in substantially different building shapes, each represented by a separate building archetype. Building archetype selection was further influenced by the concurrent development of urban patterns (UP scale, section 3.2.4). Emphasis was placed on developing those building archetypes that would also be present within selected urban patterns. 45 In total, 12 building archetypes were identified and modeled (3D and BES), with many of the building archetypes carried forward for use at the LS and UP scales. For building energy simulations, all archetypes were modeled for four orientations (i.e. the primary facade oriented to each cardinal direction). Stacked building archetypes were additionally modeled for three glazing ratios (low, moderate and high) to capture variations in facade design. All buildings were assigned standard activity, construction, system and occupant characteristics based on current BC Building Code (BCBC) and average local conditions. The building archetypes, detailed modeling methods and study findings at the BA scale are described in Chapter 4. Detailed data on each building archetype is provided in Appendix A. Table 3.2 and the following paragraphs summarise the primary urban form characteristics addressed at the BA scale. At the BA scale, four urban form characteristics are addressed: •	 Building	shape: A building’s compactness is a key building morphology factor for thermal transmission, as more compact building shapes enclose more building volume with less surface area through which heat can escape. Compactness is measured as the surface to volume ratio, or the ratio between total building surface area (i.e. exterior wall, roof and ground-contact surfaces) and total enclosed building volume:   (S total /V building ), commonly abbreviated S:V.   Compactness is specific to and constant for a given building archetype for the purposes of this research, and is based on the archetype’s footprint (i.e. plan shape) and height.   Building shape is also categorized for this project by roof shape. Roof shape plays a large role in solar potential, an indicator of local energy generation potential identified for this project (see Section 3.3.2.3). Roof shapes are categorised as: simple peaked; complex peaked; and flat (Figure 3.6). •	 Glazing	distribution: Glazing distribution represents the proportion of a building’s glazing allocated to each facade. For the purposes of this research, glazing distribution is qualitatively described using three general glazing distribution conditions: concentrated; parallel; and uniform (Figure 3.7).   Glazing distribution is specific to and constant for a given building archetype for the purposes of this research. When glazing distributions are varied, distinct building archetypes are created. 46 simple peaked complex peaked at •	 Glazing	ratio: The amount of glazing has a significant impact on building energy demand, particularly in terms of heat loss through glazed surfaces and successful implementation of passive strategies (e.g. passive heating, daylighting). Glazing ratio measures the proportional amount of building glazing – i.e. the ratio between total glazing area and total building surface area (A glazing /S total ).   The amount of glazing is specific to and constant for a given building archetype, based on typical building design conditions for the region. •	 Orientation: Building orientation impacts a building’s opportunities for passive heating and active solar energy technologies. Orientation is measured in degrees from due north orientation (0°) of the main (entrance) facade.   Building archetype energy simulations were carried out for four orientations at 90° increments. Figure 3.6: Roof shapes, illustrated example 47 uniform glazing (4 primary glazed facades) parallel glazing (2 primary glazed facades) concentrated glazing (1 primary glazed facade) Figure 3.7: Glazing distribution, illustrated example 48 To test the sensitivity of the BA scale modeling results, two building characteristics related to urban form were further examined for a subset of the building archetypes: •	 Glazing	ratio: Although the amount of glazing has been previously identified as an urban form characteristic specific to a given building archetype, sensitivity tests adjusting the amount of glazing were carried out for stacked building archetypes, where facade design can vary substantially depending on contextual factors such as building vintage.   For stacked building archetypes, three glazing ratios (low, moderate, and high) were modeled (3D and BES). The selected glazing ratios are specific to each building archetype and represent a typical range for that building type (Figure 3.8). •	 Building	activity: As identified in the literature review, building activity (e.g. residential and commercial uses) has a significant effect on how urban form characteristics impact building energy.   Building activity is measured as the ratio of commercial floor area to total floor area (A commercial /A building ).   The building activity variable is only considered in the subset of building archetypes that represent building morphologies common to mixed or fully commercial uses (See Chapter 4). When building activity is considered, the archetype is modeled for 0%, 25% (i.e. mixed use) and 100% commercial floor area with any remaining building floor area modeled as residential use. Building activity characteristics are assigned in the energy simulation model (VE-Pro) and do not affect any urban form characteristics of the building archetypes for the purposes of this project. Figure 3.8: Glazing ratio variation, illustrated example low glazing moderate glazing high glazing 49 3.3.3 Local shading (LS) scale As reflected in the literature review, the physical context (i.e. urban structure) within which a building is located will affect building energy primarily through solar access limitations (i.e. building shading). In many instances, urban structure can be highly complex, with many different shading conditions (e.g. different building shapes, heights and spacing), making it difficult to discern clear relationships between urban form characteristics and building energy. At the same time, the urban pattern (UP) scale models necessary to capture this complexity involve substantial processing times for energy simulation, limiting the number of urban structure variations that can be realistically considered within the scope of a project. Due to the time demands of UP scale energy simulations, it was important to determine a process by which to limit the total number of urban patterns to be modeled, while still maximizing the number of urban form variations considered. For this reason, a series of parametric analysis studies were completed at the local shading (LS) scale. The LS scale refers to an individual building archetype together with the structures directly adjacent to each building archetype facade. Building energy is only modeled for the individual building archetype (i.e. unidirectional analysis, Figure 3.1). For the purposes of this thesis, parametric analysis refers to the incremental variation (within a given range) of one urban form characteristic, while all others are held constant. Using this type of analysis, the effects of changing the selected urban form characteristic can be isolated. Table 3.2: Urban form characteristics at the building archetype (BA) scale Characteristic Measure Comments Building Shape Compactness surface to volume ratio Roof shape descriptive 3 categories (flat, simple peaked, complex  peaked) Glazing Distribution descriptive 3 categories (concentrated, parallel,  uniform) Ratio % glazed surface area 3 values (low, moderate, high); stacked  archetypes only Orientation Orientation  degrees 4 values; 90° increments; 0° refers to north  orientation for main facade Additional Characteristics Building activity % commercial floor area 3 values (0%, 25% and 100% commercial  floor area);non‐commercial floor area  assumed to be residential; varied for select  archetypes only 50 Parametric analysis was completed at the LS scale using individual building archetypes and directly adjacent structures. In total, 1,656 model runs were completed, including 720 core simulations (12 building archetypes x 5 UHAs x 3 adjacent building heights x 4 orientations). The remaining simulations encompassed a variety of sensitivity tests including building activity and building envelope performance. Specific methods and findings from the parametric analysis studies are presented in Chapter 5. Findings from the parametric analysis were used to inform choices regarding the development of the urban patterns modeled in Chapter 6. For example, a finding on the effects of building spacing on heating demand in high-rise buildings informed the development of a high-rise residential pattern variation increasing building spacing for reduced heating energy consumption. In this way, rather than running a number of pattern-scale energy simulations varying high-rise building spacing, only one pattern capturing the desired effect was required.  Table 3.3 summarises the primary urban form characteristics addressed at the LS scale. At the LS scale, one urban form characteristic is addressed: •	 Urban	structure:	For the purposes of this thesis, urban structure is measured by the urban horizon angle (UHA), or the angle of elevation of an adjacent building from the centre of a given facade (Baker and Steemers, 2000). UHA is a useful measure of urban structure, as it combines two key properties of urban structure – building height and building spacing (i.e. tan(UHA)=h/w) (also see Figure 2.1).   As discussed in the literature review, UHA is mathematically related to other common measures of building arrangement, including sky view factor and height to width ratio. UHA was selected for its prominent use in the literature; however, any of the measures would have been a sufficient metric of building arrangement for the thesis.   For this research, UHAs between 15° and 75° were modeled, at 15° increments. UHAs below 15° have been shown to have negligible effects on building energy (Baker and Steemers, 2000), and UHAs above 75° were outside the range occurring in the urban form conditions considered for this project.   Adjacent building height was also varied for the parametric analysis, measured as the ratio between the height of the building archetype and the height of adjacent buildings. •	 Building	morphology:	As the building archetypes modeled for the LS and BA scales are the same, building morphology characteristics are still intrinsically considered at the LS scale. 51 Building shape and glazing distribution are inherent to each building archetype; all stacked building archetypes are modeled using identified moderate glazing ratios. Orientation is modeled at 90° increments. To test the sensitivity of the LS scale modeling results, two building characteristics not associated with urban form are also analysed for a subset of the building archetypes: •	 Building	activity: As with the BA scale, building activity (e.g. residential and commercial uses) has a significant effect on how urban form characteristics impact building energy at the LS scale.   Building activity is measured as the ratio of commercial floor area to total floor area (A commercial /A building ).   The building activity variable is only considered in the subset of building archetypes that represent building morphologies common to mixed or fully commercial uses (See Chapter 4). When building activity is considered, the archetype is modeled for 0%, 25% (i.e. mixed use) and 100% commercial floor area with any remaining building floor area modeled as residential use. Building activity characteristics are assigned in the energy simulation model (VE-Pro) and do not affect any urban form characteristics of the building archetypes for the purposes of this project. •	 Envelope	performance: Variations in envelope performance both above and below current BC Building Code standards were modeled for a subset of building archetypes to explore the impact of envelope performance on the magnitude of urban form effects on building energy. 3.3.4 Urban pattern (UP) scale Urban patterns, for the purposes of this project, can be defined as spatially explicit representations of urban form, assembled from building archetypes and representative street configurations, and capturing key attributes of complex urban structure over a larger land area. Urban patterns in this project range in size from approximately nine hectares (300m by 300m) to 14 hectares (400m to 350m), or six to eight blocks. The UP scale can be considered closely associated with “neighbourhood-scale” urban form and energy modeling, although this term is not used in the project, due to the challenges and ambiguities in defining “neighbourhood” boundaries (Lynch, 1981; Jenks and Dempsey, 2007). 52 To describe urban form conditions, the research builds on the pattern-based approaches to urban form representation previously described in the literature review, using a bottom-up method of assembling archetypal representations of buildings, streets and other land uses (Miller and Cavens, 2008). Assembled from these elements, each urban pattern represents a simplified and rationalized version of an urban development type identified by shared or similar urban form characteristics (Marshall, 2005c; Habraken, 2000). The consideration of the UP scale in this research enables the study of urban form types that are recognizable, specific and relevant to a local context, but also focused on key characteristics of building morphology and urban structure. Unlike the BA and LS scales, the UP scale enables the study of complex urban structures representative of real-world urban development, including building arrangements within street patterns, varying densities of development and varying building shapes and orientations. The UP scale further enables multi-directional analysis of urban structure, shading and associated effects on building energy. The UP scale is included in two studies (Chapters 4 and 6). In Chapter 4, urban patterns are used to describe and analyse existing urban form conditions within the Metro Vancouver region. In Chapter 6, the UP scale is used to apply key findings from the research to evaluate infill development options along a transit corridor. Specific methods and findings from the UP scale analyses are presented in Chapters 4 and 6. Detailed data on the urban patterns included in this research is provided in Appendix B. At the UP scale, building morphology and urban structure become more difficult to describe and quantify, as many different conditions may be occurring within the same urban pattern. For Table 3.3: Urban form characteristics at the local shading (LS) scale Characteristic Measure Comments Urban Structure Urban horizon angle  degrees 5 values; 15° increments Adjacent building height height ratio (adjacent building height to  study building height) 3 values (0.75, 1.0, 1.5) Additional Characteristics Building activity % commercial floor area 3 values (0%, 25% and 100% commercial  floor area);non‐commercial floor area  assumed to be residential; varied for select  archetypes only Envelope performance % change from current BCBC 3 values (low, code, high); varied for select  archetypes only 53 example, UHAs will vary from building to building as building shapes, heights and spacing vary for localized conditions. For that reason, urban form characteristics at the UP scale are described by a separate set of urban form measures, appropriate to the UP scale. Table 3.4 summarises the primary urban form measures captured in the urban pattern models. Quantitatively, the UP scale is described primarily in terms of development density, captured by a combination of three measures. Cheng et al. (2006) identify that floor area ratio (FAR) and coverage are both aggregate, urban scale density measures closely tied to solar access. In addition, this project uses an estimate of residential units per hectare (uph) to describe UP scale density, as this measure is widely used and understood among planning and urban design professionals. In addition to density measures, urban patterns are described quantitatively by the amount of commercial floor area (as a percent of total floor area) as an explanatory variable for differences in building energy performance relating to building activity. Qualitatively, the urban patterns are further described by the mix of building archetypes present in the pattern, as well as the street configuration within which the building archetypes are arranged. These qualitative descriptors provide additional information on the arrangement and spacing of buildings, complimentary to the quantitative measures of density above. Street configuration in particular plays a large role in determining building arrangement and orientation at the UP scale, impacting each building’s UHAs and consequent building shading and access to solar energy (Baker and Steemers, 2000; Ratti et al., 2005; Cheng et al., 2006). At the UP scale, the quantitative urban form measures are: •	 Development	density:		Three quantitative measures of density are used in the analysis of the urban patterns: floor area ratio (FAR), coverage and residential density (uph).  Although each of these is a measure of density, each captures separate aspects of urban form. Quantitative urban pattern descriptions using FAR and coverage have previously been used in solar access studies by Cheng et al. (2006).   Floor Area Ratio (FAR): For this project, FAR refers to the gross FAR for all buildings. FAR is calculated as the ratio of total floor area to gross pattern land area, including streets (A building / A land )   The gross FAR of a given pattern is related to both building heights and building coverage, and describes overall building densities. This number has direct relationships to thermal 54 performance and potential energy loads for district energy systems. It should be noted that FAR alone does not imply a particular building shape.  Rather, depending on street configuration and building coverage similar neighbourhood densities by FAR may encompass a variety of building shapes and arrangements.   Coverage: For this project, coverage refers to the ratio of building footprint area to gross pattern land area (A footprint /A land ) (Cheng et al., 2006), or the proportional amount of land covered by buildings.   Coverage is related to the form of individual buildings within each pattern as well as building spacing. Changes in coverage can affect surface to volume ratios of individual buildings even among patterns sharing other similar urban form characteristics. Coverage will impact building shading and solar access as heights and distances between buildings change.   Residential density: Residential density refers to the total number of residential units per unit of land area. For this project, the measure of residential density is units per hectare (uph). Residential density is estimated for this project using assumptions for the total number of residential units per building type. For apartment buildings, this assumption is based on typical unit sizes in the region.   Residential density alone does not imply a particular building shape; however, this measure is commonly understood and referenced by professionals involved in decision making regarding urban form and was therefore included in the presentation of research findings (Chapters 4 and 6). •	 Building	activity: Building activity has already been described at the BA and LS scales.  The activities occurring within the building archetypes for a given pattern have a significant effect on how urban form characteristics impact building energy. For this reason, building activity will be described for each urban pattern, measured as the ratio of commercial floor area to total floor area (A commercial /A building ).   Building activity varies across patterns, often in relation to a pattern’s density, building morphology and other considerations. For example, at the lowest densities, a mix of commercial and residential activities is not a commonly occurring pattern for the region. Similarly, high density patterns tend to have a greater amount of commercial activities.  55  Considering building activity variations for the selected patterns illustrates differences in the effects of urban form changes on residential versus non-residential buildings, and allows for additional exploration in varying energy load scenarios for potential district energy systems. Qualitative or spatial urban form characteristics at the UP scale are: •	 Building	morphology: Building morphology characteristics are typically captured at the scale of the individual building archetypes, including plan shape, building height, compactness, glazing ratio and glazing distribution. At the UP scale, building morphology is described by the types and mix of building archetypes present within the pattern. •	 Street	configuration: Street configuration sets the initial structure within which building arrangement occurs. The street configuration of a pattern determines block size and orientation that in turn affects parcel size, building shape and building placement opportunities. Varying street configurations will impact building orientation and arrangement options, affecting opportunities for solar access. 3�3�5 Urban form 3D modeling Urban form conditions at the BA, LS and UP scales were modeled three-dimensionally using SketchUp (sketchup.google.com), a 3D building modeling software widely used for architecture, urban design and other applications. SketchUp was chosen for this research based on its ability to quickly model 3D building forms, and the availability of software plug-ins that allow building geometry created in SketchUp to be directly imported to building energy simulation programs. Table 3.4: Variables included in pattern scale building energy analysis Characteristic Measure Comments Density Floor area ratio total floor area to gross land area ratio Coverage % land area covered by building footprints Residential density estimated residential units per land area  (hectares) Additional Characteristics Building activity % commercial floor area measured for each pattern Qualitative Measures Building morphology descriptive type(s) and mix of building archetypes in  pattern Street configuration descriptive street configuration of pattern 56 Although building construction and activity properties can be set for building models within SketchUp using the appropriate software plug-ins, these properties were set subsequently within the building energy simulation program (VE-Pro) where more control over model settings is provided. Orientation was also set in VE-Pro. Building archetypes were modeled (3D) using protocols specified for the VE-Pro energy simulation software. Documentation on the VE-Pro website provides detailed SketchUp modeling guidelines to enable the simulation software to import building model geometry correctly (Integrated Environmental Solutions, 2009). For example, VE-Pro requires that all conditioned spaces be fully enclosed volumes, and that windows and doors be constructed using a particular representation method. Building archetype models were saved to a library of components for use in subsequent 3D modeling at the LS and UP scales. LS scale 3D modeling used the building archetypes previously modeled and added solid obstructions adjacent to the building archetype in the forms and positions defined for the parametric analysis studies defined in Chapter 5. All building archetypes and obstructions were modeled on a flat plane. Due to the abstract nature of the LS scale parametric studies, topography was excluded from 3D and BES modeling. Urban patterns were modeled (3D) in SketchUp by assembling individual, pre-modeled street configurations (also prepared by the author, see Chapter 4) and the building archetype components developed for the project. Street and building components were selected and arranged for each pattern according to the spatial requirements defined in Chapters 4 and 6. Because the urban patterns are representations of urban form that are not tied to particular geographic locations, topography was excluded from consideration in the 3D and BES modeling. 3.4 Energy Simulation Energy simulations were completed for each of the urban form conditions modeled (3D) at the BA, LS and UP scales. Appendix D summarizes all model runs and results. The simulations were carried out using VE-Pro, described in detail below. Standard assumptions were used as inputs into the energy simulations for all building characteristics not related to urban form, such as the thermal properties of building envelopes and lighting and appliance energy loads (Appendix C). 3.4.1 VE-Pro simulation tool Energy simulation modeling was undertaken using Integrated Environmental Solutions‘ (IES) VE-Pro, a suite of building performance analysis tools including thermal performance simulation, 57 solar shading and daylight analysis applications among others (www.iesve.com). Although VE-Pro is predominantly used for building energy simulation, the software is also adding capabilities for evaluating other aspects of sustainability, such as life-cycle costing and compliance tools. VE-Pro applications reference a core three-dimensional, geometric building model to which application- specific data is attached (Crawley et al., 2005). With the recent development of software plug-ins for SketchUp and Autodesk Revit, the VE-Pro model can be used in conjunction with architectural and urban design modeling software and is specifically intended to be used at all stages of design, including early planning and feasibility studies where the software can run with just a small number of inputs. This functionality makes the software ideal for urban form studies, where many details of building design may be unknown or undetermined. The tool is also well-suited to modeling multiple pattern variations, as a Building Template Manager application enables packages of construction, system and occupancy attributes to be saved and quickly applied in new simulations. The energy simulation components of VE-Pro have been under development since 1994 and meet ANSI/ASHRAE standards for energy simulation tools (Integrated Environmental Solutions, 2004; see Judkoff, 2004 for description of standard). The software is more regularly used in Europe than in North America and is commonly used in the UK (Jacobs and Henderson, 2002) where it is approved for use in meeting building performance regulation requirements (Sustainable Energy Authority of Ireland, 2011; Department for Communities and Local Government, 2011). VE-Pro has also been used in a variety of academic and peer-reviewed applications (e.g. Muhaisen and Gadi, 2006; Hamza, 2008; Short, et al., 2010) for both conceptual and highly detailed studies. This research uses three of the available VE-Pro analysis tools: ModelIT, the building geometry editor; SunCast, the 3D solar analysis tool; and ApacheSim, the primary thermal simulation software within the suite. Specific use of these applications is discussed in the following sections. 3.4.1.1 ModelIT ModelIT is the geometric building modeling component of VE-Pro. The building geometry stored in this application is referenced by all other simulation and analysis tools associated with the software package. ModelIT is capable of handling highly complex building geometry, and does not limit the number of surfaces, zones, etc. that can be simulated (Crawley et al., 2005). Building geometry can be created and edited within ModelIT, derived from detailed architectural drawing files, or imported directly from SketchUp or Autodesk Revit with the use of software plug-ins. For this research, building geometry at the BA, LS and UP scales was imported from SketchUp. For the import process to function correctly, 3D models were created using a defined language of 58 materials and geometric rules (Integrated Environmental Solutions, 2009). For example, all interior spaces were built as fully enclosed volumes to be read as “rooms” and all windows were assigned a material with a transparency between 1% and 99%. Using this language, the models could be cleanly imported into VE-Pro, with all interior volumes, exterior surfaces and fenestrations defined and indexed for further analysis. Once imported into VE-Pro, the ModelIT application uses a nested hierarchy of building elements to organize and assist in navigating the geometric model. The highest level of aggregation within the hierarchy is a “room,” defined as a fully enclosed volume within the model. Each room can be disaggregated to its enclosing surfaces (i.e. roofs, walls and floors), and each surface to its wall assemblies and fenestrations. This nested hierarchy allows for attributes (e.g. thermal properties, materials) to be assigned either to entire rooms or to individual components. To simplify high numbers of rooms in complex models, a grouping function enables rooms sharing similar characteristics to be aggregated into groups defined by the user.  This function was used for the urban pattern models, with groupings based on room use (e.g. commercial or residential) and construction type. These groupings enabled construction, system and occupancy characteristics to be assigned and edited quickly over large numbers of buildings. 3.4.1.2 SunCast SunCast performs shading and solar insolation analysis for one or multiple rooms selected from the geometric building model and is able to assess shading effects among several buildings. The SunCast tool can also be used to generate images and animations of shading conditions across time and seasons. Data outputs from the SunCast analysis can be linked to ApacheSim for thermal analysis to more accurately calculate solar heat gains. SunCast analysis does not link to lighting demand; therefore changes to urban structure and shading conditions does not impact artificial lighting demands. This was not considered an issue for the research, which focuses on heating and cooling demand for the simulation results. The SunCast solar analysis application is designed as a simple tool, with few user inputs required. The simulation uses the defined building geometry, building orientation (as set in ModelIT), and the selected geographic location of the building to calculate shading. Users input only a monthly design day and range of months to be analysed and select whether diffuse shading will be considered. SunCast analysis on all urban form models for this research was conducted for a 12 month time period, using a design day of the 15th day of each month. Diffuse shading was included in the analysis. 59 Solar analysis for this project was used primarily to inform thermal analysis; however, the application also provides internal and external insolation data, disaggregated to each building surface. Insolation data is provided as actual unshaded surface area (m2) or percent of surfaces that are unshaded. This data is useful for quantifying the amount of building surface area adequately exposed to solar insolation for renewable energy generation purposes, such as solar hot water or photovoltaics. 3.4.1.3 ApacheSim ApacheSim, VE-Pro’s dynamic thermal simulation model, is capable of running simulations ranging from one day to a year using time steps as small as one minute. Using real weather data, ApacheSim deals separately with each aspect of heat transfer and control processes involved in building thermal performance (Integrated Environmental Solutions, n.d.), with radiation, convection and conduction processes modeled for individual building elements. The primary inputs to ApacheSim thermal calculations are: the defined building geometry; the established building location and associated weather data (in this case, Vancouver, British Columbia, Canada); building construction assemblies; occupancy characteristics; internal gain assumptions; infiltration and ventilation rates; and system characteristics. To simplify input requirements, VE-Pro provides a Building Template tool that enables packages of input settings to be selected once and stored for application in multiple simulations. These templates can then be assigned individually or collectively to single building elements, rooms or groups. Templates were of particular value for this research, where building design and occupancy characteristics not related to urban form, (e.g. U-values, heating system efficiencies, appliance loads and behavioural assumptions) were held constant across many building archetypes and urban patterns. Specific information about templates used for this research is provided in the following sections. Detailed information on specific settings included in the templates can be found in Appendix C. 3.4.1 4 Construction templates Construction templates can be created in VE-Pro for roofs, ceilings, external walls, internal partitions, ground floors and fenestrations. Each template represents one assembly, and includes data on materials, thicknesses and thermal properties. Construction templates are stored in the Apache Constructions Database, which also provides a number of default constructions including standard ASHRAE construction assemblies. For this project, building construction assemblies were selected or created to meet current British Columbia Building Code (BCBC) requirements for the associated building use and height. The BCBC 60 uses ASHRAE 90.1 (2004) standards for all residential buildings greater than four stories and non- residential buildings greater than three stories, and the ASHRAE assemblies provided in VE-Pro were used when possible. For low-rise residential and commercial construction, assemblies were created based on current construction practices. All assemblies were confirmed to meet BCBC through a comparison of calculated and code-required R-values for each assembly. 3.4.1.5 Thermal condition templates Thermal Condition Templates in VE-Pro represent a wide variety of internal building conditions including heating and cooling systems, occupancy rates and schedules, internal gain assumptions, and air exchanges. To the extent possible, these conditions were left at default values and, where reasonable, settings were held constant between building archetypes. Specific settings include: • Heating and hot water system type, heating set point and system efficiency were held constant for all building types (e.g. central heating, radiator, natural gas at 89% efficiency). • Residential occupancy assumptions were calibrated with average household sizes by residential unit type, according to the 2006 Census of Population for British Columbia. • Lighting and appliance loads were assigned by building use and occupancy, based on VE-Pro defaults. Lighting loads are not linked to the SunCast model; therefore, changes in urban structure and shading do not change artificial lighting requirements. • Air conditioning system and cooling set point were included for all building types to track changes in cooling demand. Air conditioning currently accounts for a small but increasing portion of energy consumption in British Columbia. Although some of the selected characteristics may not accurately reflect the systems or loads used in current construction (e.g. the same heating system type for all buildings), reduced complexity among non-urban form variables was determined to be more valuable to the project than more precise representations of internal building attributes. As will be discussed in Chapter 4, the overall energy consumption results are still consistent with other data sources, particularly for the comparative, rather than predictive, goals of the thesis. 3�4�2 Ve-Pro outputs Simulation results from VE-Pro are accessible in a variety of ways and provide data for a number of energy related factors and metrics. Outputs include: energy demand; system loads; system sizes; comfort statistics; surface temperatures; humidity; heat gains by source; and others. Many of the results can also be disaggregated to an individual room or group of rooms. The ability to 61 disaggregate the simulation results was an important factor for analysis at the UP scale, allowing for single buildings within the pattern to be evaluated both individually and as a part of the larger urban area. For the analysis conducted in this research, the primary data outputs taken from the simulations were space heating and cooling demand as well as two indicators of renewable energy potential: solar potential and district energy potential. Each of these measures is described below. Other building energy uses such as lighting, appliance loads and hot water demand were not measured for the project, as they comprise a relatively minor component of total building energy and are less influenced by urban form characteristics. References to “building energy performance” and “heating demand” in the thesis thus refer to space conditioning or space heating only. 3.4.2.1 Space heating demand Space heating demand measures the amount of heat energy required to maintain the desired temperature within the building. Space heating demand does not reflect the actual amount of energy supplied to the building, which is also a factor of energy source and building heating system efficiency. Space heating demand is presented for this project in four unit measures, used for different purposes throughout the analysis: • GJ (total) measures the total space heating demand of buildings and patterns • GJ/m2 measures the annual heating demand per unit of building floor area and allows average thermal efficiency to be compared across buildings or patterns of differing sizes and densities • GJ/cap measures the annual heating demand per capita and allows average thermal efficiency to be compared across patterns as a function of how efficiently building floor area is occupied • GJ/ha measures the annual heating demand per unit of land area (hectares) and allows average thermal efficiency to be compared across patterns as a function of how efficiently land area is used (this “heating demand density” measure is especially relevant for district energy applications) 3.4.2.2 Cooling demand Space cooling demand measures the amount of heat energy required to be removed to maintain the desired temperature within the building. Space cooling demand does not reflect the actual amount of energy consumed in the cooling process, which is also a factor of energy source and building 62 cooling system efficiency. Additionally, a large number of buildings in Metro Vancouver do not use mechanical cooling, such that cooling demand may not reflect energy consumption at all. However, this number does capture increases in the internal temperature of the modeled buildings, which could result in the increased use or installation of mechanical cooling. Like heating demand, space cooling demand is measured for the project as: GJ (total); GJ/m2; GJ/cap and GJ/ha. 3.4.2.3 Solar potential Solar potential is used as an indicator of local energy generation potential, and is calculated using outputs from the VE-Pro simulations. Solar potential, for the purposes of this project, measures the total amount of horizontal or south-facing unshaded roof area. This area (Asp) represents the roof area available for solar energy technologies (e.g. solar hot water or photovoltaic systems). It should be noted that roof surfaces with other orientations (e.g. east or west), as well as vertical surfaces such as walls, can be suitable for solar technologies in certain applications; however, this project focuses on south-facing roof surfaces as a simplified indicator of solar potential. No minimum “useable” roof area threshold was established for this indicator although, in application, roof areas under a certain size may be deemed unsuitable for particular solar technologies, such as solar hot water panels. Solar potential does not include passive heating (i.e. solar heat gains) as these gains are already accounted for in heating demand analysis using ApacheSim. Solar potential is used in the thesis as a broad estimate of opportunities for the application of solar energy technologies, whether in generating heat or electricity. Energy supplied by these technologies has the potential to reduce a building’s consumption of natural gas or electricity for hot water or to meet a portion of a building’s electricity demand, depending on the technology installed. The actual amount of electricity or heat energy contributed to buildings from solar energy systems would vary greatly depending on the type and efficiency of the installed technology, and projecting energy performance results would be further complicated by assumptions on how the solar energy is used. Accordingly, solar potential is not measured directly in terms of energy in this analysis and is not applied explicitly to space conditioning or allocated to other building energy demands. Rather, by tracking the solar potential indicator across existing urban patterns and potential pattern variations, critical lessons regarding the connection between urban form choices and neighbourhood-scale capacity for solar energy technologies can be discerned. Solar potential is measured for the project as Asp (m 2), Asp as a proportion of total roof area (Asp/Aroof), and the ratio of Asp to building floor area (Asp/Afloor). 63 3.4.2.4 District energy potential District energy potential, for the purposes of this project, is a second indicator for local energy generation potential, and is calculated using outputs from the VE-Pro simulations. District energy potential is a measure of the annual heating demand density (GJ/ha) for a given urban pattern. Higher heating demand densities enable more efficient energy plant sizing and use, as well as more efficient installation and use of associated infrastructure. In application, preliminary assessments for district energy feasibility typically include estimates of heating demand density as well as assessments of planned future development, existing building or district scale hydronic systems, locally available energy sources and available locations for energy plants, among other factors (Compass Resource Management 2008 and 2009). As a majority of the factors beyond heating demand density are location and context specific, they are unable to be measured for the representative urban patterns, which are not tied to a particular location. For this reason, heating demand density alone is used for the initial assessment of district energy potential. District energy potential as defined in this project is not a direct output from the VE-Pro tool but is a secondary indicator derived from simulation results. District energy potential is calculated by dividing the total heat demand (space heating and hot water, as quantified by the ApacheSim thermal simulation model) by pattern land area. As this indicator is tied to land area, it is only measured at the UP scale. These measures and indicators serve as the dependent variables for the data analysis described in Section 3.4. 3�5 Analysis of energy and Urban form Using the urban form measures described in Section 3.2 as independent variables and the VE- Pro outputs (i.e. energy performance data) described in Section 3.3 as dependent variables, relationships between urban form and building energy are able to be described and quantified at three scales (i.e. BA, LS and UP)(Chapters 4 through 6). However, as each of these chapters considers a different scale or combination of scales of urban form, each chapter also applies different approaches to the analysis of results. In Chapter 4, both the BA and UP scales are examined for existing conditions in the Metro Vancouver region; however, at both scales, only a small number of urban form conditions (i.e. 12 building archetypes and 6 urban patterns) are modeled. For this small number of conditions, additional statistical analysis (e.g. regression analysis) was not completed; rather comparisons are drawn and trends identified across modeled building archetypes and urban patterns individually. Likewise, 64 Chapter 6, which examines the energy performance of eight additional urban patterns centered on a given development context (i.e. a transit corridor), draws comparisons and identifies trends across individual UP scale results. As noted in Section 3.2.2, Chapter 4 also includes sensitivity analysis for the 12 building archetypes for glazing ratios and building activity. In Chapter 5, linear regression was used to describe the relationships between individual urban form variables and building heating demand, cooling demand and local energy generation indicators. R-squared values were calculated for each relationship to quantify relationship strength. Influence coefficients (IC) were calculated for each urban form measure and associated energy outcome to “quantify the influence of one variable on another” (Spitler et al. 1989). An influence coefficient (IC) is calculated by approximating partial derivatives by dividing a change in results by a change in the parameter of interest: Linear regression analysis using influence coefficients or related methods has been applied in a number of studies exploring the effects of design variables on building energy consumption, primarily at the building scale (e.g. Baker and Steemers 2000; Spitler et al. 1989; Capozzoli et al. 2009; Lam and Hui 1996; Lam and Wan 2008; Li et al. 2008; Taveres and Martins 2007). These methods enable a simple approach to exploring the relative contributions of selected design parameters to building energy performance. Chapter 5 uses regression analysis to establish urban form-energy trends over a much larger data set, resulting from the parametric analysis completed at the LS scale, using individual building archetypes and directly adjacent structures. Chapter 5 also includes additional sensitivity analysis, testing how regression results change as building activity and building energy performance change. 3.6 Limitations of the Research This research intentionally uses a number of strategies to limit the scope of the thesis and to focus the results on the impacts of urban form on building energy performance. Among these strategies, the thesis examines a limited number of urban form conditions within a specific regional and climatic context. A number of key influences on building energy performance such as building envelopes, system efficiencies and occupant behaviour, have been simplified and held constant to isolate urban form effects. Further, to control for external factors and to allow for the consideration of conditions other than those currently existing in the region, models (both 3D urban form models �� � � ����������������� � ∆������ ∆���������  65 and energy simulation tools) have been used to represent the conditions measured. While these strategies were necessary to meet the objectives of the research, they also create several important limitations: • The modeled building archetypes and urban patterns represent a limited subset of urban forms and do not cover all types and variations of urban form in the region, nor do they represent many of the subtleties reflected in individual neighbourhoods or individual building designs. • The modeled building archetypes and urban patterns have been selected to represent urban form conditions relevant to the context of the Metro Vancouver region. While many of the urban forms considered in the research are common to much of North America and other areas internationally, the applicability of the research findings is limited to those areas with similar urban form characteristics. • The simulated energy results are in part a product of the climate data input into the energy model, representing average climatic conditions for the Metro Vancouver region (see Appendix C). This further limits the applicability of these findings for other climatic zones, where differences in solar gain and other conditions may significantly shift results. • The use of urban patterns as representative forms of development and the reduced number of inputs within the energy simulations intentionally focus the research on a select and limited subset of the factors influencing building energy consumption. Therefore, the numbers presented in the research are for comparative purposes only, and do not reflect the actual energy consumption of specific neighbourhoods or buildings. • The energy simulation results and subsequent analysis are limited to certain aspects of building energy, namely building heating and cooling demand. Within the scope of this research, other building energy considerations, such as daylighting and natural ventilation, have been excluded due to time constraints and limitations of the selected simulation tool. Future inclusion of these issues has the potential to change research findings, particularly for commercial buildings, for which lighting and ventilation are significant energy demands. • The approaches to modeling and analysis used for this research necessarily assume that the urban form factors act independently from other factors affecting building energy, such that potential influences between urban form, building materials, building systems and occupant 66 behaviour are not considered. In reality, the problem of building energy is multi-variable and will involve multiple interactions and synergies which could amplify the importance of urban form (Ratti et al., 2005). • The accuracy of the results and findings are limited by the accuracy of the urban form models and energy simulations. If the energy simulations have not responded reliably to changes in the urban form conditions, then the relationships only describe the functionality of the model and not the actual conditions. However, the general agreement between the existing pattern energy simulations and real world conditions suggest that model accuracy is sufficient. 67 cHAPter 4: eXIstInG UrbAn forM conDItIons This chapter presents the description and analysis of twelve building archetypes (BA scale) and six urban patterns (UP scale) currently existing within the Metro Vancouver region.  This component of the research was undertaken as an initial testing and exploration of both existing regional urban conditions and the modeling methods developed for the project, including both urban form (3D) modeling and building energy simulation (BES). The results presented in the chapter represent early and, at times, incomplete findings. As such, this chapter is not intended to provide conclusive evidence of the relationships between urban form and building energy, but should be read as an interim step in identifying patterns and potential relationships to be considered further in the subsequent chapters. As identified by Steinitz (1990), one of the critical tasks in landscape and planning research is to understand how the system under consideration works, including the “structural relationships among its elements.”  Beginning this research with the analysis of select existing building archetypes and urban patterns provides modeled urban form and energy performance data reflective of current regional urban form conditions. Modeled existing conditions can then be used as a baseline from which urban form variations are explored. For example, the analysis and 3D modeling of the existing urban patterns helped to establish the range of building spacing and urban horizon angles modeled in the following chapter (Chapter 5).  The existing building archetypes and urban patterns have been selected and modeled (3D and BES) according to the general processes outlined in Sections 3.2 and 3.3. Section 4.1 provides additional detail on selecting and modeling the existing building archetypes and patterns. Section 4.1 also describes the selected building archetypes and urban patterns in detail and provides quantitative measures of their urban form characteristics. Section 4.2 provides results from the energy simulations for the existing urban patterns and associated building archetypes. Section 4.3 discusses the implications of these results. Findings from the analysis of the existing building archetypes and urban patterns show general agreement with other sources of building-scale and neighbourhood-scale energy data and relationships for the region, indicating that the simulation tools and assumptions applied in the research are sufficient to provide reliable results. The findings further highlight key interrelationships between urban form characteristics that carry over to the parametric and pattern-scale analyses of urban form completed in the following chapters. 68 4�1 Methods The general building archetype and urban pattern 3D modeling and energy simulation methods have been described in Chapter 3. This section provides additional detail on the way that these methods were applied for the analysis of twelve building archetypes and six urban patterns representing existing conditions in Metro Vancouver. Sections 4.1.1 and 4.1.2 describe how existing building archetypes and urban patterns were selected for study. Sections 4.1.3 and 4.1.4 briefly review the 3D and BES modeling approaches used for the project and discuss specifically how modeling was completed for the building archetype and urban pattern studies for existing conditions in the region. 4.1.1 Selection of building archetypes Building archetypes were developed to represent typical buildings in the Metro Vancouver region. Building archetypes were selected with the intent to differentiate between key urban form characteristics while still keeping the total number of archetypes as small as possible. Emphasis was placed on selecting and modeling those building archetypes that would also be present within selected urban patterns (Section 4.1.2). As a majority (approximately 70%) of the region’s developed land (i.e. excluding roads, utilities, agricultural, protected natural and other undeveloped areas) is residential (Metro Vancouver, 2008), the selection of building archetypes was limited initially to residential buildings. The inclusion of commercial activities for the energy simulation of selected building archetypes is discussed in Section 4.1.4. The approach to the identification and selection of building archetypes was adapted from methods used in the Elements of Neighbourhood database (elementsdb.sala.ubc.ca) developed by Kellett and Girling (2012) at the University of British Columbia. The database categorization approach uses a nested hierarchy of increasingly specific land use and urban form characteristics highly amenable to the description of building archetypes in energy analysis applications. The approach breaks out building archetypes first by land use (i.e. building activity), secondly by general building form (i.e. detached, attached, and stacked) and thirdly by more detailed morphological characteristics such as compactness and glazing. Using a similar method of categorization for this research helped to ensure that a full spectrum of building archetypes was included, covering the range of BA scale urban form characteristics (i.e. compactness, roof shape, glazing distribution and glazing ratios). Figure 4.1 illustrates the range of selected archetypes and their urban form characteristics. In total, 12 building archetypes were identified and modeled (3D and BES), with many of the building archetypes carried forward for use in analysis at the LS and UP scales. The selected building archetypes are summarised in Table 4.1. Detailed data on each building archetype is provided in Appendix A. As noted in Section 3.2.2, variations in glazing ratio and building activity were modeled 69 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 10% 20% 30% 40% 50% LR Apt U-plan HR Apt Tower LR Apt Narrow LR Apt Wide LR Apt Single Rowhouse SF Wide Dpx Wide G la zi ng  R at io Surface to Volume Ratio (S:V) STACKED ATTACHED DETACHED at roofs peaked roofs SF Narrow Dpx Narrow Rowhouse C SF Narrow C uniform glazing parallel glazing concentrated glazing for subsets of the building archetypes to more fully account for existing conditions and practice occurring in the region. These variations are not considered separate archetypes; variations are noted in Table 4.1 where applicable. 4.1.2 Selection of existing urban patterns Six “existing” urban patterns have been selected and modeled (3D and BES) for this research. These patterns and associated results from the energy simulations provide urban form and energy performance data reflective of current regional urban form conditions. Modeled existing conditions can then be used as a baseline from which urban form variations are explored. The patterns selected to represent existing urban form conditions occurring in Metro Vancouver were chosen based on four criteria: 1) Selected patterns should represent dominant types of development in the region 2) Each selected pattern should be distinctly different in at least one urban form characteristic, Figure 4.1: Building archetypes and urban form characteristics 70 such as density, mix of building types, street configuration and/or building activity 3) Together, the collection of selected patterns should represent a broad range of typical urban patterns present in the region 4) The total number of selected patterns should be kept as small as possible to maximise the simplicity and clarity of research results and in consideration of processing times for energy simulations Based on the first two criteria, six preliminary patterns were chosen for further development (Table 4.2). These preliminary patterns were identified as known patterns of development occurring in the region, ranging from low-density, single family residential development to high-density urban centres. Each preliminary pattern was subsequently developed in detail as a 3D urban pattern model using real-world data (Section 4.1.2.1). The developed 3D urban pattern models were then evaluated collectively to ensure that criteria 3 and 4 were also met (Section 4.1.2.2). Table 4.1: Building archetypes and variations Archetype General Form Compactness  (S:V) Roof  Shapea Glazing  Distributionb Glazing  Ratio Building  Activity SF Narrow Detached 0.778 S P 11% None None SF Narrow C Detached 0.778 S C 11% None None SF Wide Detached 0.773 C C 12% None None Dpx Narrow Attached 0.790 S P 15% None None Dpx Wide Attached 0.755 C C 12% None None Rowhouse Attached 0.535 S P 18% None None Rowhouse C Attached 0.535 S C 16% None None LR Apt Single Stacked 0.432 F C 24% 18% (Low) None 36% (High) LR Apt Narrow Stacked 0.357 F U 34% 28% (Low) None 40% (High) LR Apt Wide Stacked 0.307 F P 30% 21% (Low) Mixed Use 46% (High) Commercial LR Apt U‐plan Stacked 0.288 F U 32% 25% (Low) None 44% (High) HR Apt Tower Stacked 0.210 F U 44% 33% (Low) Mixed Use 59% (High) Commercial aRoof Shape: S = simple peaked roof; C = complex peaked roof; F = flat roof bGlazing Distribution: C = concentrated; P = parallel; U = uniform Sensitivity Variations Glazing  Ratio Building Morphology 71 Table 4.2: Preliminary patterns and urban form characteristics (qualitative) Preliminary Urban Form (UF)  Criteria 1  Criteria 2   Pattern Example Location Characteristics Dominant in region? Distinct urban form? Tributary, Fleetwood, Surrey, BC Density: Low Yes Single Family Bldg Activity: Residential only  Bldg Mix: < 3 archetypes Street Config: Tributary Yes, primarily  outside City of  Vancouver Grid,  Sunset, Vancouver, BC Density: Low Yes Yes Single Family Bldg Activity: < 5% commercial  Bldg Mix: < 3 archetypes St t C fi G idree   on g: r Mixed,  East Clayton, Surrey, BC Density: Medium Yes Ground Oriented Bldg Activity: Residential only Bldg Mix: 3 to 5 archetypes Less dominant,  but recent  approach to new        Street Config: Mixed Grid,  Kitsilano, Vancouver, BC Density: Medium Yes Yes Ground Oriented Bldg Activity: > 5% commercial       residential  development Bldg Mix: 3 to 5 archetypes Street Config: Grid Grid, Low‐rise  Arbutus, Vancouver, BC Density: High Yes Yes Multi‐family Bldg Activity: > 5% commercial Bldg Mix: > 5 archetypes Street Config: Grid Grid, High‐rise  West End, Vancouver, BC Density: High Yes Multi‐family Bldg Activity: > 5% commercial Bldg Mix: > 5 archetypes Street Config: Grid Less dominant,  primarily near  downtown  Vancouver and  major transit 72 4.1.2.1 Urban pattern development For each of the six preliminary patterns, two to three locations representative of each pattern were identified within the region. Urban form characteristics for these locations (e.g. building types, street configuration, building arrangement, building spacing, etc.) were analysed and the results used as “rules” for the development of the 3D urban pattern models. For example, spacing between buildings in the 3D urban pattern models was determined by the typical building spacing occurring in locations representative of that pattern. Each representative location was analysed using publicly available municipal data, orthophotos and site visits (as needed). Location analysis consisted of: building inventories (noting the type and form of each building); measuring, or collecting quantitative data on, building spacing and setbacks; and noting typical street pattern, building orientation and relative locations of buildings (e.g. higher density buildings along major roads, mix of detached and attached building types on same block, etc). To the extent possible, quantitative analysis (e.g. mean building spacing, percentage mixes of building types) was used to help identify and establish shared urban form characteristics (Marshall, 2005a); however, the inherent subjectivity of this process must be acknowledged. Certainly, initial choices regarding what locations to measure in the region, the grouping of locations based on shared qualities, and the determination of quantitative or form-based ranges for what is considered “typical” are the interpretation of the researcher.  As noted by Marshall (2005c) and Kostof and Castillo (1992), there is no clear, absolute set of urban patterns. Rather, urban form is a “morphological continuum (Marshall, 2005a), where classification depends on the intended application and must seek to balance between too few and too many categories for the intended purpose. Acknowledging that urban pattern selection is subjective and that an absolute set of patterns does not exist additionally requires the acknowledgement that the patterns selected for this project will not fully nor completely accurately represent urban form conditions present in Metro Vancouver. This will not substantially affect the results of the research for three reasons: 1) Limiting the scope of the project to urban conditions in Metro Vancouver is primarily a mechanism for limiting the number of patterns considered; the primary purpose of the research is not to quantify building energy consumption for Metro Vancouver, but to capture relationships between energy performance and changes to urban form. 2) Further variations in urban form, including detailed parametric analysis of LS scale urban 73 form characteristics, are considered in the following chapters (Chapters 5 and 6) and serve to address additional urban form-energy relationships that may be missed in the selected existing urban patterns. 3) The six selected urban patterns are distinct and cover a wide range of urban form conditions, as illustrated in the following section. This selection of patterns is sufficient to illustrate differences in building energy performance, regardless of applicability to the Metro Vancouver context. 4.1.2.2 Evaluating urban pattern selection Using an approach similar to the categorisation of building archetypes in the preceding section, selected patterns were evaluated to ensure that the range of UP scale urban form characteristics (i.e. density, building activity, building morphology (i.e. mix of building types) and street configuration) was addressed (Figure 4.2) and that a sufficient variety of urban form conditions were included in the selected patterns. Figure 4.2: Urban patterns and urban form characteristics Co ve ra ge  ( % ) Floor Area Ratio (FAR) 1 archetype 2 to 4 archetypes GRID AND MIXED STREET CONFIGURATION TRIBUTARY STREET CONFIGURATION 5+ archetypes 0 0.5 1.0 1.5 2.0 2.5 15% 20% 25% 30% 35% Grid Single Family Grid Low-rise Multi-family Grid High-rise Multi-family Tributary Single Family Mixed Ground Oriented Grid Ground Oriented 74 The selected urban patterns representing current regional urban form conditions are summarised in Table 4.3. Each pattern is named according to its street configuration (Section 4.1.3.1) and its primary building type. Further contextual information on each pattern is included in the following paragraphs. Detailed data on each urban pattern is provided in Appendix B. 4.1.2.3 Tributary, Single Family (TSF) pattern The Tributary, Single Family (TSF) pattern is a dominant residential pattern outside of the City of Vancouver. The TSF pattern exists primarily in areas of more recent development, particularly in municipalities on the edges of the region (e.g. Surrey, Delta, Langley) that have or had available, undeveloped land. Variations on the TSF pattern are also found across the North Shore and in municipalities including Coquitlam and Port Moody, where topography discourages regular street patterns. The TSF pattern does not typically include commercial uses, as commercial land designations tend be clustered at centralised locations along major arterial roads. 4.1.2.4 Grid, Single Family (GSF) pattern The Grid, Single Family (GSF) pattern represents a dominant residential pattern in the City of Vancouver and select areas of surrounding municipalities. The City of Vancouver is comprised of a primarily gridded street system, with blocks oriented along an east-west axis. Outside of the downtown peninsula, major corridors and select comprehensive development zones, nearly all land within the City is zoned for single family dwellings (City of Vancouver, 2011d). The GSF pattern often includes a small number of multi-family and/or mixed use buildings along major roads and transit routes. 4.1.2.5 Mixed, Ground Oriented (MGO) pattern The Mixed, Ground Oriented (MGO) pattern is less common in the region, but has been used as an alternative to the more common TSF pattern of single family development prevalent in Metro Vancouver. A regional example of the MGO pattern is the East Clayton development within the City of Surrey. Unlike the TSF pattern, the MGO pattern incorporates a more interconnected street system, a greater mix of housing types, smaller lots and tighter building spacing, resulting in higher residential densities while maintaining predominantly ground-oriented building forms. The existing MGO pattern does not include commercial uses, as the regional examples of this pattern primarily cluster commercial at centralised locations along major arterial roads, similar to the TSF pattern. Variations of the MGO pattern, including examples with a much higher mix of commercial and residential uses, have been referred to as “new urbanist” development (Congress for the New Urbanism, 2001), a pattern of development now being used in many cities throughout North America. 75 4.1.2.6 Grid, Ground Oriented (GGO) pattern The Grid, Ground Oriented (GGO) pattern represents key areas of incremental residential infill throughout the City of Vancouver and surrounding areas. The GGO pattern is characterized by a mix of ground-oriented single family detached, duplex and rowhouse building forms that have primarily been redeveloped over time from single family housing stock. Areas experiencing this type of densification tend to be located near major transportation routes and supported by mixed-use, high density corridors integrated within the pattern. Examples of this pattern include parts of the Kitsilano neighbourhood and areas around Commercial Drive in the City of Vancouver. 4.1.2.7 Grid, Low-Rise Multi-Family (GLM) pattern The Grid, Low-Rise Multi-Family (GLM) pattern represents key multi-family residential areas within the City of Vancouver and throughout the region. Low-rise residential buildings (e.g. three to four stories) in the region include a large number of mid-20th century buildings typically developed in discrete pockets near transit routes, such as in the Marpole neighbourhood or Lower Lonsdale in the City of North Vancouver. Recently in the City of Vancouver, newer developments such as Arbutus Walk and the Olympic Village have reflected an increased interest in returning to low and mid- rise (e.g. five to 12 stories) residential building types as opposed to the “Vancouver-style” (see for example Price, 2003; Punter, 2003; Boddy, 2009) tower-and-podium design. This direction has been specifically noted in the Cambie Corridor Plan, which states “Emphasising mid-rise building forms… the plan introduces a new form of urbanism for the City of Vancouver that signals an evolution from the podium tower forms that have defined the downtown peninsula” (City of Vancouver, 2011a). 4.1.2.8 Grid, High-Rise Multi-Family (GHM) pattern The Grid, High-Rise Multi-Family (GHM) pattern represents a less common urban pattern across the region by land area; however, this pattern has become an important component of the region’s identity in regards to high-density living (Punter, 2003; Harcourt et al., 2007). Variations of the GHM pattern are prolific across the downtown peninsula in the City of Vancouver and in key transit locations (e.g. at Skytrain stations) throughout the region. In some instances (e.g. the West End and Downtown neighbourhoods in Vancouver and Central Lonsdale in the City of North Vancouver), high-rise residential towers (e.g. 12+ stories) have been constructed incrementally as infill. Other areas, including Coal Harbour, Yaletown, and several Skytrain station developments have been established as part of larger, comprehensive plans. 76 Table 4.3: Existing urban patterns Tributary, Single Family Similar neighbourhood Fleetwood, Surrey, BC Street Configuration Tributary Density, units/ha (units/acre) 11.7 (4.7) Floor area ratio 0.286 Coverage (%) 19% Urban Form (UF) CharacteristicsPattern   Commercial floor area (%) 0% Grid, Single Family Similar neighbourhood Sunset, Vancouver, BC Street Configuration Grid Density, units/ha (units/acre) 19.3 (7.8) Floor area ratio 0.395 Coverage (%) 25% Commercial floor area (%) 3% Mixed, Ground Oriented Similar neighbourhood East Clayton, Surrey, BC Street Configuration Mixed Density, units/ha (units/acre) 20.6 (8.3) Floor area ratio 0 433    . Coverage (%) 28% Commercial floor area (%) 0% Grid, Ground Oriented Similar neighbourhood Kitsilano, Vancouver, BC Street Configuration Grid Density, units/ha (units/acre) 42.3 (17.1) Floor area ratio 0.531 Coverage (%) 31% Commercial floor area (%) 5% Grid, Low‐rise Multi‐family Similar neighbourhood Arbutus, Vancouver, BC Street Configuration Grid Density units/ha (units/acre) 144 5 (58 5),    .   . Floor area ratio 1.321 Coverage (%) 31% Commercial floor area (%) 5% Grid, High‐rise Multi‐family Similar neighbourhood West End, Vancouver, BC Street Configuration Grid Density, units/ha (units/acre) 245.7 (99.5) Floor area ratio 2.279 Coverage (%) 33% Commercial floor area (%) 2% 77 4�1�3 Urban form 3D modeling The general approach to urban form 3D modeling for the project has been previously discussed in Chapter 3. The twelve building archetypes and six existing patterns were modeled three- dimensionally using SketchUp. Building archetypes were modeled (3D) using protocols specified for the VE-Pro energy simulation software and saved to a library of components for use in subsequent 3D modeling of the existing urban patterns. Urban patterns were modeled (3D) in SketchUp by assembling individual, pre-modeled street configurations (Section 4.1.3.1) and the building archetype components developed for the project. Street and building components were selected and arranged for each pattern according to spatial “rules” defined through the analysis of regional locations representative of each pattern (Section 4.1.2.1). Because the urban patterns are representations of urban form that are not tied to particular geographic locations, topography was excluded from consideration in the 3D and BES modeling. Each 3D urban pattern model of existing regional conditions represents a land area of approximately nine hectares (approximately 300 by 300 meters). This pattern size was selected to balance the need to sufficiently represent UP scale urban form characteristics with the time demands inherent in running energy simulations on large geometric models. At an area of nine hectares, the 3D pattern models are able to represent variations in building shapes, arrangements and development densities over multiple blocks, across streets, and from parcel to parcel. The nine hectare 3D pattern models result in energy simulation times of approximately two days per model, including geometry imports, solar analysis and thermal analysis. 4.1.3.1 Street configurations Extensive work on characterizing street patterns has been completed by Marshall (2005a). Among his methods of categorization, Marshall illustrates that street configurations can be described by their relative properties of connectivity and complexity, although in reality, street configurations occur in a continuum of these and other characteristics. For this research, the street configurations associated with the selected urban patterns fall clearly into three typical structures: tributary (low connectivity, low complexity); grid (high connectivity, low complexity) and mixed (intermediate complexity and connectivity). Within the City of Vancouver, the dominant street configuration is a rectangular, east-west oriented grid with access lanes. Newer, low-density residential developments in the region are typically organized around tributary street configurations with wide arterial roads and residential streets 78 ending in cul-de-sacs. A mixed street configuration, characterized by irregular street and intersection intervals, a high level of connectivity and some lane access, is less common in the region but has been increasingly used in residential and mixed use developments. The three street configurations used for the existing urban patterns were modeled as 2-dimensional surfaces using SketchUp and saved to a library of components for use in the existing patterns. The three street configuration models are shown in Figure 4.3. Each configuration is bounded by roads, with a larger, arterial road at the north edge. Including an arterial road in each street configuration provided the opportunity to capture pattern relationships between road type and building placement (e.g. higher density buildings next to major roads). 4.1.4 Building energy simulation The six existing urban patterns and the twelve building archetypes were evaluated for energy performance (i.e. heating demand, cooling demand, solar potential and district energy potential) using the VE-Pro simulation tool (Section 3.3).  Standard assumptions were used as inputs into the energy simulations for all building characteristics not related to urban form, such as the thermal properties of building envelopes, lighting requirements and appliance energy loads (Appendix C). Appendix D summarizes all model runs and results. For BA scale energy simulations, all archetypes were modeled for four orientations (i.e. the primary facade oriented to each cardinal direction). Archetypes were assigned standard construction, system and occupancy properties based on current BC Building Code (BCBC). Inputs not regulated Figure 4.3: Urban pattern street configurations 79 by the BCBC, such as the number of occupants per unit, were based on averages for the region. Completing energy simulations for each building archetype individually was instrumental in providing explanatory data for the pattern scale results and will be discussed further in Section 4.2. To account for facade variations among existing buildings, stacked building archetypes were modeled (BES) for two additional glazing ratios (one lower and one higher than the ratio selected for the archetype) to capture the effects of variations in facade design (Table 4.1, above). Changes in the amount of glazing are made in SketchUp, prior to importing model geometry into VE-Pro. Two building archetypes (i.e. LR Apt Wide and HR Apt Tower) were also modeled for two additional building activity mixes (25% and 100% commercial floor area) to capture the effects of building activity on energy consumption. These two archetypes were selected for building activity variations due to the frequent use of these building forms in residential, mixed use and commercial applications. Building activity is varied within VE-Pro by changing each building’s thermal condition templates (Section 3.3.1.5). Thermal condition templates are created in VE-Pro and represent a wide variety of internal building conditions including heating and cooling systems, occupancy rates and schedules, internal gain assumptions, and air exchanges. For this project, building geometry and construction types are held constant across building activity variations for a given archetype. Pattern scale energy simulations were completed for one orientation, with each pattern’s arterial road oriented north. Building archetypes within each pattern were grouped by archetype using VE-Pro’s grouping function to facilitate the assignment of construction, system and occupancy properties. Where mixed use or commercial buildings occurred in the patterns, these buildings were grouped separately and assigned the required thermal condition templates to capture commercial building activity. 4�2 results and Discussion The results of the energy simulation modeling for the twelve building archetypes and six existing patterns are presented below. As discussed at the beginning of this chapter, the intent of this analysis, which took place early in the research process, was both to test the methods identified for the project and to define the existing, regional urban form and building energy conditions from which the research develops. 4.2.1 Heating demand For the six existing patterns, total space heating demand and space heating demand per hectare increase as the amount of floor area per area of land (FAR) intensifies (Table 4.4). The heating 80 demand intensity (GJ/ha) of each pattern is particularly relevant in determining the feasibility of district energy applications and is discussed more in Section 4.3.4. Heating demand per capita primarily decreases as pattern density increases (by FAR) with the exception of the GHM pattern (Figure 4.4). The general decrease is related in part to the benefits of compact building shapes, but is also tied closely to the more efficient use of floor area to accommodate the estimated population. On average, smaller, higher density units in Metro Vancouver accommodate fewer residents per unit, but also accommodate a higher population per square meter of floor area (Table 4.5). Figure 4.4: Annual urban pattern space heating demand per capita (GJ/cap) Table 4.4: Annual space heating demand by pattern Pattern FAR Total (GJ) GJ/ha GJ/capita GJ/m² TSF 0.286 4293.6 483.0 13.32 0.169 GSF 0.395 5440.8 621.1 11.23 0.157 MGO 0.433 6691.5 745.2 11.95 0.172 GGO 0.531 6945.5 792.9 8.14 0.149 GLM 1.321 12317.1 1406.1 5.40 0.106 GHM 2.279 31371.4 3581.2 8.10 0.157 H ea tin g de m an d (G J/ ca pi ta ) 0 2 4 6 8 10 12 14 0.5 1.0 1.5 2.0 2.5 TSF GSF MGO GGO GLM GHM Pattern: FAR 81 Heating demand per square meter of building floor area also generally decreases as pattern density (by FAR) increases, with the exception of the GHM pattern (Figure 4.5). However, this relationship is weaker than that found with per capita results. The lower correlation between density and heating demand per square meter can also be seen with energy simulations of the individual building archetypes (Figure 4.6 and Table 4.6), where simulation results show that attached building forms (i.e. duplex and rowhouse buildings) require more heat energy per building area than other residential building forms. These results are counter to the general understanding that high density neighbourhoods, compact building shapes and shared walls and floors provide thermal performance benefits and therefore reduce building energy consumption. The simulated results for the building archetypes likewise contradict the average trends in the Province for space heating energy per square meter by residential unit type (Figure 4.7) which show heating demand reductions for higher density building forms. However, an examination of additional factors embedded in the modeling of the existing patterns and building archetypes assists in explaining these discrepancies. Table 4.5: Typical residential unit size and average household size Archetype Typ. Unit Size (m²) Avg. Household Sizea Population per m² SF Narrow 229 3.1 0.0135 SF Narrow C 229 3.1 0.0135 SF Wide 244 3.1 0.0127 Dpx Narrow 122 3.0 0.0246 Dpx Wide 143 3.0 0.0210 Rowhouse 118 2.7 0.0229 Rowhouse C 118 2.7 0.0229 LR Apt Single 73 1.9 0.0260 LR Apt Narrow 73 1.9 0.0260 LR Apt Wide 73 1.9 0.0260 LR Apt U‐plan 73 1.9 0.0260 HR Apt Tower 73 1.7 0.0233 aBased on 2006 census data for Metro Vancouver 82 Figure 4.5: Annual urban pattern space heating demand per unit of floor area (GJ/m2) H ea tin g de m an d (G J/ m 2 ) 0 0.02 0.5 1.0 1.5 2.0 2.5 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 TSF GSF MGO GGO GLM GHM Pattern: FAR Table 4.6: Annual space heating demand by building archetype Archetype Compactness (S:V) Glazing Ratio Floor Area (m²) Total (GJ) GJ/m² SF Narrow 0.778 11% 229 36.18 0.158 SF Narrow C 0.778 11% 229 36.78 0.160 SF Wide 0.773 12% 244 40.42 0.165 Dpx Narrow 0.790 15% 245 42.35 0.173 Dpx Wide 0.755 12% 285 43.86 0.154 Rowhouse 0.535 18% 474 81.84 0.173 Rowhouse C 0.535 16% 474 79.29 0.167 LR Apt Single 0.432 24% 1,561 236.53 0.152 LR Apt Narrow 0.357 34% 2,044 261.04 0.128 LR Apt Wide 0.307 30% 3,642 362.40 0.100 LR Apt U‐plan 0.288 32% 7,585 734.31 0.097 HR Apt Tower 0.210 44% 12,077 1915.76 0.159 83 H ea tin g de m an d (G J/ m 2 ) 0 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 HR  A pt To we r LR  A pt U- pla n LR  A pt W ide LR  A pt Na rro w LR  A pt Sin gle Ro wh ou se  C Ro wh ou se Dp x W ide Dp x N arr ow SF  W ide SF  N arr ow  C SF  N arr ow detached dwellings attached dwellings stacked dwellings orientation: N E S W Figure 4.6: Annual building archetype space heating demand per unit of floor area (GJ/m2) Figure 4.7: Annual space heating demand per unit of floor area (GJ/m2), Provincial data 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 De tac he d Dw ell ing s At tac he d Dw ell ing s St ac ke d Dw ell ing s H ea tin g de m an d (G J/ m 2 ) 84 First, a simple energy simulation test using a constant residential unit (Figure 4.8) to isolate the thermal effects of shared walls and floors illustrates that there are significant benefits from increasing the number of shared surfaces (up to 48% reductions in space heating demand per square meter in the Metro Vancouver climate). This implies that there are other factors included in the building archetypes and urban patterns that are affecting the simulation results. At the BA scale, one of these factors is the amount of glazing assumed for each building archetype. Glazing ratios have been considered previously in building energy studies (Baker and Steemers, 2000; Cobalt Engineering, 2009), which find significant increases in building energy consumption for glazing ratios above 40% for office buildings in the UK and residential buildings in Vancouver, respectively. The negative building energy consequences of high glazing ratios is also reflected in the ASHRAE Standard 90.1 (2004) building code requirements, where buildings with glazing ratios greater than 50% cannot comply with the standard prescriptive requirements of the code. For the 2007 version of ASHRAE, this limit has been reduced to 40% (Home Owner Protection Office, 2011). Energy simulations of the building archetypes show a positive correlation between heating demand per square meter and surface to volume ratio. However, the plot of this data shows that the relationship between compactness and energy consumption is weaker for low surface to volume ratios (Figure 4.9). This result can be accou