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A Simulation-based Research on Passive District
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 104 (2016) 257 – 262
CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems
A simulation-based research on passive district Yinan Zhoua, Xinyu Taoa, Perry Pei-Ju Yanga,b,* a
Sino-U.S.Eco Urban Lab, College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China b Eco Urban Lab, College of Architecture, Georgia Institute of Technology, Atlanta 30309, U.S.A
Abstract This paper is aiming to propose a new design concept named passive district which assists designer to improve district energy efficiency and reduce energy demand by smart urban design without mechanical dynamics method. Furthermore, specific simulation-based experiment is implemented to demonstrate the research methodology on correlation between urban form and energy performance, in which the workflow is summarized as follows: definition of constants and variables, modeling, simulation, data sorting and decision making. This urban modeling integrates the morphological factors together including grid size, floor area ratio, cover ratio, passive zone ratio, surface to volume ratio and so on and compares the energy impact respectively. Particularly a method of multi-disciplinary design optimization (MDO) is applied to analyze how multiple factors affect energy use comprehensively and support scientific decision making. Finally, as conclusion the application potential of passive district is discussed. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
© 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection peer-review of under responsibility of CUE Peer-reviewand/or under responsibility the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. Keywords: Urban form; Energy consumption; Passive district; Multi-disciplinary design optimization; Simulation
1. Introduction: from architecture to district Design strategies for green architecture can be categorized into two branches, passive design strategies and active design strategies. Passive architectural energy-saving technique is strategy that utilize no or very few mechanical devices but passively absorb or directly utilize renewable energy to reduce energy consumption [1,2]. Energy issue is the core to distinguish passive technique and active technique. Energy consumption in building, however, is not only influenced by building performance but also inevitably influenced by surrounding urban context or natural environment. These influences consist of shading from adjacent buildings, the air ventilation condition determined by urban structure, urban heat island effect, etc. In other words, even the same building may perform obviously differently when locates in high-density are and low-density area. Only considering of single building in simulation without * Corresponding author. Tel.: +1-404-894-2076; fax: +1-404-894-2076. E-mail address: [email protected].
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. doi:10.1016/j.egypro.2016.12.044
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Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
impact from urban context would result in great inaccuracy. Fortunately the concept of ecological district originating from New Urbanism makes people rethink the energy issue in district level comprehensively, the middle scale between architecture and city. On one hand, district is the fundamental unit that connects urban space with human sense, providing us with integrated environment experience; on the other hand, comparing with the entire city, district is easier to be realized and controlled for development. This research is aiming to propose the concept and define the research framework of passive distirct and take an experiment based on ideal model simulation in the climate condition of Shanghai as examples to explore the correlation between urban form and energy use. The research methodology on energyefficient urban design is established during the process. 2. Research question 2.1. Concept definition Passive district is defined as that in the planning process the district utilizes no or very few mechanical dynamic equipment but passively absorbs or directly utilizes renewable energy to reduce transportation energy, infrastructure energy and building energy in terms of achieving energy efficiency. It is required that the performance of impact factors, such as district capacity, structure, texture, building typology, urban mixed use and other related aspects, should be evaluated and compared with its competing alternatives respectively according to human behaviors and interior and exterior climate adaptability so that the feedback can be used to direct design optimization.
Fig. 1. Research framework of passive strategy
2.2. Research framework From the perspective of urban planning, the concern towards passive neighborhood is supposed to address three questions as follows. The first question is how to build up a scientific model on the scale of district to analyze the correlation between urban morphological factors and energy use. The second one is how to instruct and optimize complicated urban design by methods of performance analysis to drive the urban form generation basing on ecological concept. The third one is how to set up a feasible research procedure of experimental research and knowledge accumulation for design optimization according to local climatic environment.
Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
Energy flow goes across the scale boundary so the energy study should include not only building and mechanical setting but also site. The research on passive district is not isolated but incorporated and inclusive in the general perspective of thermodynamics, which should focus on the analysis of energy flow. Generally, the research objects of passive district consist of two parts, the fundamental content and the extension (Fig. 1). The former represents the adoption of design strategy can reduce the energy use directly by accommodating to the climate, sunlight, wind corridor, water flow, human behavior etc. For example, the street network in hot region can rotate a small angle to prevent the direct sunlight and adapt to prevailing wind direction. Yet the latter means the strategy that serves as organizing active strategy while energy performance is improved by coordinating with energy equipment or promoting the utilization of renewable energy, such as the urban structure adapted to distributed energy system. Urban design is more complex and integrated than architecture design. The principle of integrating multi-disciplinary objectives should be carried out to avoid hampering the profit of other stakeholders. So the design process should be constituted to an intact loop by four phases: experimental research, adaptive design, integrated design according to multiple principles and performance evaluation. It is a dynamic process with consecutive feedback and adjustment. 2.3. Literature review Related researches on correlation between urban form and energy performance starting from 1960s generate on-going influence on subsequent research. Such morphological factors include urban density, urban fabric and building typology, urban mixed use, etc. With respect to urban density, Norman et al [3] evaluated the energy use and greenhouse gas emission by means of life cycle analysis and found that the energy consumption per capita in the low-density areas in Toronto was 1.5 times of that in the highdensity areas. Salat and Morterol [4] compared five factors of urban blocks of Paris in 18th, 19th and modern time. In aspect of urban fabric and building typology, Martin and March’s research [5] is enlightening. They raised the question that what is the best land use and extracted six basic building types after investigation. The successors’ research focused mostly on evaluation of the environmental performance of urban form, such as Gupta’s research [6] on thermal performance of non-air-conditioned buildings in hot climate. Ratti et al [7] reviewed and reevaluated the six building types by innovative computer techniques, focusing on the solar potential and morphological factors. For urban mixed use, the travel demand is largely reduced in consequence of the adjacency of diverse urban functions such as restaurant, office and residential building, satisfying with the need of daily life [8]. Nevertheless, the existing researches focus on exploring the specific technical strategy for relationship analysis but the systematical research methodology especially applied to instruct urban design in the preliminary stage and decision making with multiple objectives and variables is relatively rare. 3. Experiment 3.1. Research methodology For exploring the correlation between urban form and energy performance, it is crucial to break down the research problem into objectives and a series of parameters including constants and variables. By analyzing how the change of variables can impact the objectives step by step, a clear approach will emerge. In this research, firstly a number of essential variables that can directly determine the urban physical form need to be selected, which are called independent variables. Each variable has its specific variation range and variation step. Then it is feasible to apply these variables to build up parametric 3D models. Due to the variation of parameters, this parametric model can generate a huge amount of urban
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Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
forms for comparison and analysis. After modeling, it is imported into integrated software, such as ModelCenter for multi-disciplinary design optimization. In this step, ModelCenter like a central nervous system connects three parts together including modeling software, simulation software and powerfully embedded statistic function. It leverages algorithms to change the variables of the model consecutively according to the previous setting of parameters and exports alternatives into simulation software [9]. Finally, the likelihood of achieving the objectives would be calculated from perspective of multiple objectives to propose a recommended urban design choice driven by performance for decision making.
Fig. 2. Illustration of Constants and Variables
3.2. Definition of constants and variables The constants in the testing model are prerequisites determined before simulation to eliminate the impact from inessential issues (Fig. 2). The constants are defined as follows: (1) Boundary size, 1.5 X 1.5 square kilometres; (2) Building typology, courtyard; (3) Building function, office building; (4) Building materials and constructions, referring to local standard; (5) Weather condition. The variables selected are significantly morphological factors. Only independent variables, that neither can be expressed by other variables nor changed due to the change of other, have the capacity to drive the generation of parametric model, including: (1) Grid size, 250m, 200m, 150m and 100m; (2) Building length. If G represents the grid size, the building length for testing can be written as 0.625G-2.5, 0.75G-5, 0.875G-7.5 and G-10 (m); (3) Building depth, 6m, 10m, 14m and 18m; (4) Building height, 9m, 27m, 45m, 63m or 99m, while the storey height is fixed to 3 meters; (5) Window to wall ratio, 0.2, 0.4, 0.6 and 0.8. As the independent variables change sequentially, the simulation loop conducts for 1280 times in total to cover all the scenarios that defined by the five independent variables and their variations. Derived from five independent variables above, there are dependent factors used for data analysis, which can figure out the relationship between urban form and energy consumption more directly, such as cover ratio, floor area ratio, surface area to volume ratio, etc. 3.3. Data analysis There are four steps for data analysis. First of all, calculate the dependent morphological factors automatically basing on independent variables. Secondly clean up the raw data. Those samples that are obviously impractical should be eliminated. Thirdly, analyze the data. In ModelCenter, implementing DOE (Design of Experiments) tool to clarify the correlation between the results and the variables and graph them out (Fig. 3). Finally, make a decision. By giving the variables and results weight values in the DOE tool based on multi-disciplinary design objective, the program will calculate and provide optimal solutions, comparing specific data for competing alternatives. The urban design should satisfy three objectives, minimizing the operational energy, maximizing human comfort of spatial perception and maximizing the floor area simultaneously. According to design objectives, as a testing alternative, the
Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
weight of energy consumption per unit area is set as -1.0; the weight of grid size is set as -0.2; the weight of building height is set as -0.2; the weight of FAR is set as 0.2.
Fig. 3. Correlation between energy consumption and morphological factors
3.4. Experiment conclusion Energy consumption per unit area has a negative relevance with grid size, building depth, building height, building length, cover ratio and floor area ratio, and has a positive relevance with and surface area to volume ratio. Within the variation range of the variables, the impact on average energy is about 63% by increasing grid size, 60% by increasing building depth, 90% by increasing building height, 10% by increasing window-wall ratio, 82% by increasing cover ratio, 96% by increasing FAR, and 96% by decreasing surface area to volume ratio. The results indicate that the morphological factors of urban context have a considerable influence on the energy consumption per unit area. It is easy to understand that in Shanghai climate condition the feature, extremely hot in summer, is obvious, while intensive development in terms of more centralized and high-density urban district can reduce the sunlight availability to achieve better energy performance. If focusing on the scatter plot of FAR-energy consumption, when FAR increases, the energy consumption decreases and the decline rate decreases as well. By further verification, it is found that these four curves represent different grid size respectively. The larger grid size owns the best energy performance. Moreover, for different grid size, the turning point of decline rate is different. When the grid sizes are 100m, 150m, 200m and 250m, respectively their corresponding turning points of decline rate are at the FAR of 2.5, 1.5, 1.0 and 0.7. If weight value is set based on data analysis above, the Pareto optimal solutions that meet the design objectives best are sifted out. According to the parallel coordinates plot generated by ModelCenter, the blue lines represent the recommended solutions while the red lines are poor ones (Fig. 4).
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Fig. 4. Multi-disciplinary design objective analysis
4. Further discussion As a path of urban design leading to energy efficiency, passive district is a valuable strategy for application. Three further discussions are inspired from the research. First, due to different climate condition, local knowledge database of passive district strategy should be set up respectively according to specific climate characteristic to conclude principle for instructing urban design. Secondly, the innovative MDO method applied in urban design changed the previous research methodology that relies on a small number of models. It sees design as a key variable in the performance-based modeling by producing substantially huge amount of urban form options for making design decisions. Massive simulation and embedded powerful statistic function is necessary for urban designer who usually needs to satisfy multiple objectives. It can balance all factors comprehensively and finally reach an all-win solution. Thirdly, the development of micro grid energy system provides new opportunities for passive district. As the extension of research framework of passive district, the active strategies interact more and more with passive ones to shape the creative urban form together. References [1] Beck CA, Florida Solar Energy Center, Cape Canaveral(USA). Designing with the environment, Volume 1,US,1980. [2] Mazria E. Passive solar energy book. Emmons Pennsylvania: Rodale Press, 1979. [3] Norman J, Maclean L, Asce M,et al. Comparing high and low residential density:life-cycle analysis of energy use and greenhouse gas emissions. Journal of Urban Planning and Development 2006; 132(1): 10-21. [4] Salat S, Mertorol A. Factor 20:A multiplying method for dividing by 20 the carbon energy footprint of cities:the urban morphology factor. Urban Morphologies Laboratory, 2006. [5] Martin L, March L. Urban Space and Structure. Cambridge Press, UK,1972. [6] Gupta A.Solar radiation and urban design for hot climates. Environment and Planning B: Planning and Design 1984; 11:435-454. [7] Ratti C, Baker N, Steemers K. Energy consumption and urban texture. Energy and buildings 2005; 37: 762-776. [8] Cervero R, Kockelman K. Travel demand and the 3Ds: Density, diversity,and design. Transportation Research Part D:Transport and Environment 1997; 2(3):199-219. [9] Basbagill JP, Flager FL, Lepech M. A muti-objective feedback approach for evaluating sequential conceptual building design decisions. Automation in Construction 2014; 45:136-150.
ScienceDirect Energy Procedia 104 (2016) 257 – 262
CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems
A simulation-based research on passive district Yinan Zhoua, Xinyu Taoa, Perry Pei-Ju Yanga,b,* a
Sino-U.S.Eco Urban Lab, College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China b Eco Urban Lab, College of Architecture, Georgia Institute of Technology, Atlanta 30309, U.S.A
Abstract This paper is aiming to propose a new design concept named passive district which assists designer to improve district energy efficiency and reduce energy demand by smart urban design without mechanical dynamics method. Furthermore, specific simulation-based experiment is implemented to demonstrate the research methodology on correlation between urban form and energy performance, in which the workflow is summarized as follows: definition of constants and variables, modeling, simulation, data sorting and decision making. This urban modeling integrates the morphological factors together including grid size, floor area ratio, cover ratio, passive zone ratio, surface to volume ratio and so on and compares the energy impact respectively. Particularly a method of multi-disciplinary design optimization (MDO) is applied to analyze how multiple factors affect energy use comprehensively and support scientific decision making. Finally, as conclusion the application potential of passive district is discussed. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
© 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection peer-review of under responsibility of CUE Peer-reviewand/or under responsibility the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. Keywords: Urban form; Energy consumption; Passive district; Multi-disciplinary design optimization; Simulation
1. Introduction: from architecture to district Design strategies for green architecture can be categorized into two branches, passive design strategies and active design strategies. Passive architectural energy-saving technique is strategy that utilize no or very few mechanical devices but passively absorb or directly utilize renewable energy to reduce energy consumption [1,2]. Energy issue is the core to distinguish passive technique and active technique. Energy consumption in building, however, is not only influenced by building performance but also inevitably influenced by surrounding urban context or natural environment. These influences consist of shading from adjacent buildings, the air ventilation condition determined by urban structure, urban heat island effect, etc. In other words, even the same building may perform obviously differently when locates in high-density are and low-density area. Only considering of single building in simulation without * Corresponding author. Tel.: +1-404-894-2076; fax: +1-404-894-2076. E-mail address: [email protected].
1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. doi:10.1016/j.egypro.2016.12.044
258
Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
impact from urban context would result in great inaccuracy. Fortunately the concept of ecological district originating from New Urbanism makes people rethink the energy issue in district level comprehensively, the middle scale between architecture and city. On one hand, district is the fundamental unit that connects urban space with human sense, providing us with integrated environment experience; on the other hand, comparing with the entire city, district is easier to be realized and controlled for development. This research is aiming to propose the concept and define the research framework of passive distirct and take an experiment based on ideal model simulation in the climate condition of Shanghai as examples to explore the correlation between urban form and energy use. The research methodology on energyefficient urban design is established during the process. 2. Research question 2.1. Concept definition Passive district is defined as that in the planning process the district utilizes no or very few mechanical dynamic equipment but passively absorbs or directly utilizes renewable energy to reduce transportation energy, infrastructure energy and building energy in terms of achieving energy efficiency. It is required that the performance of impact factors, such as district capacity, structure, texture, building typology, urban mixed use and other related aspects, should be evaluated and compared with its competing alternatives respectively according to human behaviors and interior and exterior climate adaptability so that the feedback can be used to direct design optimization.
Fig. 1. Research framework of passive strategy
2.2. Research framework From the perspective of urban planning, the concern towards passive neighborhood is supposed to address three questions as follows. The first question is how to build up a scientific model on the scale of district to analyze the correlation between urban morphological factors and energy use. The second one is how to instruct and optimize complicated urban design by methods of performance analysis to drive the urban form generation basing on ecological concept. The third one is how to set up a feasible research procedure of experimental research and knowledge accumulation for design optimization according to local climatic environment.
Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
Energy flow goes across the scale boundary so the energy study should include not only building and mechanical setting but also site. The research on passive district is not isolated but incorporated and inclusive in the general perspective of thermodynamics, which should focus on the analysis of energy flow. Generally, the research objects of passive district consist of two parts, the fundamental content and the extension (Fig. 1). The former represents the adoption of design strategy can reduce the energy use directly by accommodating to the climate, sunlight, wind corridor, water flow, human behavior etc. For example, the street network in hot region can rotate a small angle to prevent the direct sunlight and adapt to prevailing wind direction. Yet the latter means the strategy that serves as organizing active strategy while energy performance is improved by coordinating with energy equipment or promoting the utilization of renewable energy, such as the urban structure adapted to distributed energy system. Urban design is more complex and integrated than architecture design. The principle of integrating multi-disciplinary objectives should be carried out to avoid hampering the profit of other stakeholders. So the design process should be constituted to an intact loop by four phases: experimental research, adaptive design, integrated design according to multiple principles and performance evaluation. It is a dynamic process with consecutive feedback and adjustment. 2.3. Literature review Related researches on correlation between urban form and energy performance starting from 1960s generate on-going influence on subsequent research. Such morphological factors include urban density, urban fabric and building typology, urban mixed use, etc. With respect to urban density, Norman et al [3] evaluated the energy use and greenhouse gas emission by means of life cycle analysis and found that the energy consumption per capita in the low-density areas in Toronto was 1.5 times of that in the highdensity areas. Salat and Morterol [4] compared five factors of urban blocks of Paris in 18th, 19th and modern time. In aspect of urban fabric and building typology, Martin and March’s research [5] is enlightening. They raised the question that what is the best land use and extracted six basic building types after investigation. The successors’ research focused mostly on evaluation of the environmental performance of urban form, such as Gupta’s research [6] on thermal performance of non-air-conditioned buildings in hot climate. Ratti et al [7] reviewed and reevaluated the six building types by innovative computer techniques, focusing on the solar potential and morphological factors. For urban mixed use, the travel demand is largely reduced in consequence of the adjacency of diverse urban functions such as restaurant, office and residential building, satisfying with the need of daily life [8]. Nevertheless, the existing researches focus on exploring the specific technical strategy for relationship analysis but the systematical research methodology especially applied to instruct urban design in the preliminary stage and decision making with multiple objectives and variables is relatively rare. 3. Experiment 3.1. Research methodology For exploring the correlation between urban form and energy performance, it is crucial to break down the research problem into objectives and a series of parameters including constants and variables. By analyzing how the change of variables can impact the objectives step by step, a clear approach will emerge. In this research, firstly a number of essential variables that can directly determine the urban physical form need to be selected, which are called independent variables. Each variable has its specific variation range and variation step. Then it is feasible to apply these variables to build up parametric 3D models. Due to the variation of parameters, this parametric model can generate a huge amount of urban
259
260
Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
forms for comparison and analysis. After modeling, it is imported into integrated software, such as ModelCenter for multi-disciplinary design optimization. In this step, ModelCenter like a central nervous system connects three parts together including modeling software, simulation software and powerfully embedded statistic function. It leverages algorithms to change the variables of the model consecutively according to the previous setting of parameters and exports alternatives into simulation software [9]. Finally, the likelihood of achieving the objectives would be calculated from perspective of multiple objectives to propose a recommended urban design choice driven by performance for decision making.
Fig. 2. Illustration of Constants and Variables
3.2. Definition of constants and variables The constants in the testing model are prerequisites determined before simulation to eliminate the impact from inessential issues (Fig. 2). The constants are defined as follows: (1) Boundary size, 1.5 X 1.5 square kilometres; (2) Building typology, courtyard; (3) Building function, office building; (4) Building materials and constructions, referring to local standard; (5) Weather condition. The variables selected are significantly morphological factors. Only independent variables, that neither can be expressed by other variables nor changed due to the change of other, have the capacity to drive the generation of parametric model, including: (1) Grid size, 250m, 200m, 150m and 100m; (2) Building length. If G represents the grid size, the building length for testing can be written as 0.625G-2.5, 0.75G-5, 0.875G-7.5 and G-10 (m); (3) Building depth, 6m, 10m, 14m and 18m; (4) Building height, 9m, 27m, 45m, 63m or 99m, while the storey height is fixed to 3 meters; (5) Window to wall ratio, 0.2, 0.4, 0.6 and 0.8. As the independent variables change sequentially, the simulation loop conducts for 1280 times in total to cover all the scenarios that defined by the five independent variables and their variations. Derived from five independent variables above, there are dependent factors used for data analysis, which can figure out the relationship between urban form and energy consumption more directly, such as cover ratio, floor area ratio, surface area to volume ratio, etc. 3.3. Data analysis There are four steps for data analysis. First of all, calculate the dependent morphological factors automatically basing on independent variables. Secondly clean up the raw data. Those samples that are obviously impractical should be eliminated. Thirdly, analyze the data. In ModelCenter, implementing DOE (Design of Experiments) tool to clarify the correlation between the results and the variables and graph them out (Fig. 3). Finally, make a decision. By giving the variables and results weight values in the DOE tool based on multi-disciplinary design objective, the program will calculate and provide optimal solutions, comparing specific data for competing alternatives. The urban design should satisfy three objectives, minimizing the operational energy, maximizing human comfort of spatial perception and maximizing the floor area simultaneously. According to design objectives, as a testing alternative, the
Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
weight of energy consumption per unit area is set as -1.0; the weight of grid size is set as -0.2; the weight of building height is set as -0.2; the weight of FAR is set as 0.2.
Fig. 3. Correlation between energy consumption and morphological factors
3.4. Experiment conclusion Energy consumption per unit area has a negative relevance with grid size, building depth, building height, building length, cover ratio and floor area ratio, and has a positive relevance with and surface area to volume ratio. Within the variation range of the variables, the impact on average energy is about 63% by increasing grid size, 60% by increasing building depth, 90% by increasing building height, 10% by increasing window-wall ratio, 82% by increasing cover ratio, 96% by increasing FAR, and 96% by decreasing surface area to volume ratio. The results indicate that the morphological factors of urban context have a considerable influence on the energy consumption per unit area. It is easy to understand that in Shanghai climate condition the feature, extremely hot in summer, is obvious, while intensive development in terms of more centralized and high-density urban district can reduce the sunlight availability to achieve better energy performance. If focusing on the scatter plot of FAR-energy consumption, when FAR increases, the energy consumption decreases and the decline rate decreases as well. By further verification, it is found that these four curves represent different grid size respectively. The larger grid size owns the best energy performance. Moreover, for different grid size, the turning point of decline rate is different. When the grid sizes are 100m, 150m, 200m and 250m, respectively their corresponding turning points of decline rate are at the FAR of 2.5, 1.5, 1.0 and 0.7. If weight value is set based on data analysis above, the Pareto optimal solutions that meet the design objectives best are sifted out. According to the parallel coordinates plot generated by ModelCenter, the blue lines represent the recommended solutions while the red lines are poor ones (Fig. 4).
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Yinan Zhou et al. / Energy Procedia 104 (2016) 257 – 262
Fig. 4. Multi-disciplinary design objective analysis
4. Further discussion As a path of urban design leading to energy efficiency, passive district is a valuable strategy for application. Three further discussions are inspired from the research. First, due to different climate condition, local knowledge database of passive district strategy should be set up respectively according to specific climate characteristic to conclude principle for instructing urban design. Secondly, the innovative MDO method applied in urban design changed the previous research methodology that relies on a small number of models. It sees design as a key variable in the performance-based modeling by producing substantially huge amount of urban form options for making design decisions. Massive simulation and embedded powerful statistic function is necessary for urban designer who usually needs to satisfy multiple objectives. It can balance all factors comprehensively and finally reach an all-win solution. Thirdly, the development of micro grid energy system provides new opportunities for passive district. As the extension of research framework of passive district, the active strategies interact more and more with passive ones to shape the creative urban form together. References [1] Beck CA, Florida Solar Energy Center, Cape Canaveral(USA). Designing with the environment, Volume 1,US,1980. [2] Mazria E. Passive solar energy book. Emmons Pennsylvania: Rodale Press, 1979. [3] Norman J, Maclean L, Asce M,et al. Comparing high and low residential density:life-cycle analysis of energy use and greenhouse gas emissions. Journal of Urban Planning and Development 2006; 132(1): 10-21. [4] Salat S, Mertorol A. Factor 20:A multiplying method for dividing by 20 the carbon energy footprint of cities:the urban morphology factor. Urban Morphologies Laboratory, 2006. [5] Martin L, March L. Urban Space and Structure. Cambridge Press, UK,1972. [6] Gupta A.Solar radiation and urban design for hot climates. Environment and Planning B: Planning and Design 1984; 11:435-454. [7] Ratti C, Baker N, Steemers K. Energy consumption and urban texture. Energy and buildings 2005; 37: 762-776. [8] Cervero R, Kockelman K. Travel demand and the 3Ds: Density, diversity,and design. Transportation Research Part D:Transport and Environment 1997; 2(3):199-219. [9] Basbagill JP, Flager FL, Lepech M. A muti-objective feedback approach for evaluating sequential conceptual building design decisions. Automation in Construction 2014; 45:136-150.