Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design
•We present a multi-objective optimization model that assist green building design.•We use an improved multi-objective genetic algorithm (NSGA-II) as theory basis.•We present a case study with the aid of the multi-objective approach. Several conflicting criteria exist in building design optimization...
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| Vydáno v: | Energy and buildings Ročník 88; s. 135 - 143 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.02.2015
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| Témata: | |
| ISSN: | 0378-7788 |
| On-line přístup: | Získat plný text |
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| Abstract | •We present a multi-objective optimization model that assist green building design.•We use an improved multi-objective genetic algorithm (NSGA-II) as theory basis.•We present a case study with the aid of the multi-objective approach.
Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA–BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA–BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. |
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| AbstractList | Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA-BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA-BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. •We present a multi-objective optimization model that assist green building design.•We use an improved multi-objective genetic algorithm (NSGA-II) as theory basis.•We present a case study with the aid of the multi-objective approach. Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA–BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA–BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. |
| Author | Li, Baizhan Wang, Di Jia, Hongyuan Yu, Wei Zhang, Ming |
| Author_xml | – sequence: 1 givenname: Wei surname: Yu fullname: Yu, Wei email: yuweicqu@gmail.com organization: Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400045, China – sequence: 2 givenname: Baizhan surname: Li fullname: Li, Baizhan email: baizhanli@cqu.edu.cn organization: Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400045, China – sequence: 3 givenname: Hongyuan surname: Jia fullname: Jia, Hongyuan email: jiahony@outlook.com organization: Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400045, China – sequence: 4 givenname: Ming surname: Zhang fullname: Zhang, Ming email: 972257180@qq.com organization: Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400045, China – sequence: 5 givenname: Di surname: Wang fullname: Wang, Di email: 460061097@qq.com organization: Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400045, China |
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| Snippet | •We present a multi-objective optimization model that assist green building design.•We use an improved multi-objective genetic algorithm (NSGA-II) as theory... Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper... |
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| SubjectTerms | Artificial neural network Building design Computer simulation Construction Design of buildings Energy consumption Genetic algorithms Mathematical models Multi-objective genetic algorithm Networks Optimization Thermal comfort |
| Title | Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design |
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