Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II
It is necessary to reduce the huge energy consumption of buildings while ensuring indoor thermal comfort. The building envelope design notably affects the energy consumption and indoor thermal comfort. This study proposes a framework of support vector regression non-dominated sorting genetic algorit...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24013996 |
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| author | Ailing Wang Ying Xiao Chunlu Liu Ying Zhao Shaonan Sun |
| author_facet | Ailing Wang Ying Xiao Chunlu Liu Ying Zhao Shaonan Sun |
| author_sort | Ailing Wang |
| collection | DOAJ |
| description | It is necessary to reduce the huge energy consumption of buildings while ensuring indoor thermal comfort. The building envelope design notably affects the energy consumption and indoor thermal comfort. This study proposes a framework of support vector regression non-dominated sorting genetic algorithm-II (SVR-NSGA-II) to study the multi-objective optimization of buildings and discover the best building envelope design. First, the data are obtained by OpenStudio 1.1.0 simulation. Then SVR is used to describe the relationships between the parameters and the two objectives. Using SVR models as the objective functions, NSGA-II is applied to optimize the objectives. Finally, taking a proposed office building as an example, the developed framework is applied, verifying its feasibility and effectiveness. The optimized parameters of the building envelope are as follows: the exterior wall U-value, roof U-value, exterior wall U-value, solar heat gain coefficient value (SHGC), and south, north, east, west window-to-wall ratios are respectively equal to 0.664 W/(m2⋅K), 0.393 W/(m2⋅K), 1 W/(m2⋅K), 0.381, 0.697, 0.7, 0.273, and 0.254. The design of the proposed building can be modified to provide a more energy efficient and comfortable building. The proposed model saves time and simplifies operation compared with traditional building information modeling methods. |
| format | Article |
| id | doaj-art-a72c8882eabf4811a41e13f88c481f64 |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-a72c8882eabf4811a41e13f88c481f642024-11-14T04:32:09ZengElsevierCase Studies in Thermal Engineering2214-157X2024-11-0163105368Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-IIAiling Wang0Ying Xiao1Chunlu Liu2Ying Zhao3Shaonan Sun4School of Management, Zhengzhou University, Zhengzhou, 450001, ChinaSchool of Management, Zhengzhou University, Zhengzhou, 450001, ChinaArchitecture and Built Environment, Deakin University, Geelong, VIC, 3220, AustraliaSchool of Management, Zhengzhou University, Zhengzhou, 450001, ChinaSchool of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China; Corresponding author.It is necessary to reduce the huge energy consumption of buildings while ensuring indoor thermal comfort. The building envelope design notably affects the energy consumption and indoor thermal comfort. This study proposes a framework of support vector regression non-dominated sorting genetic algorithm-II (SVR-NSGA-II) to study the multi-objective optimization of buildings and discover the best building envelope design. First, the data are obtained by OpenStudio 1.1.0 simulation. Then SVR is used to describe the relationships between the parameters and the two objectives. Using SVR models as the objective functions, NSGA-II is applied to optimize the objectives. Finally, taking a proposed office building as an example, the developed framework is applied, verifying its feasibility and effectiveness. The optimized parameters of the building envelope are as follows: the exterior wall U-value, roof U-value, exterior wall U-value, solar heat gain coefficient value (SHGC), and south, north, east, west window-to-wall ratios are respectively equal to 0.664 W/(m2⋅K), 0.393 W/(m2⋅K), 1 W/(m2⋅K), 0.381, 0.697, 0.7, 0.273, and 0.254. The design of the proposed building can be modified to provide a more energy efficient and comfortable building. The proposed model saves time and simplifies operation compared with traditional building information modeling methods.http://www.sciencedirect.com/science/article/pii/S2214157X24013996Building energy consumptionMulti-objective optimizationNon-dominated sorting genetic algorithm-II (NSGA-II)Support vector regression (SVR)Thermal comfort hour |
| spellingShingle | Ailing Wang Ying Xiao Chunlu Liu Ying Zhao Shaonan Sun Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II Case Studies in Thermal Engineering Building energy consumption Multi-objective optimization Non-dominated sorting genetic algorithm-II (NSGA-II) Support vector regression (SVR) Thermal comfort hour |
| title | Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II |
| title_full | Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II |
| title_fullStr | Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II |
| title_full_unstemmed | Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II |
| title_short | Multi-objective optimization of building energy consumption and thermal comfort based on SVR-NSGA-II |
| title_sort | multi objective optimization of building energy consumption and thermal comfort based on svr nsga ii |
| topic | Building energy consumption Multi-objective optimization Non-dominated sorting genetic algorithm-II (NSGA-II) Support vector regression (SVR) Thermal comfort hour |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X24013996 |
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