Performance optimization of high-rise residential buildings in cold regions considering energy consumption
Abstract With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-ris...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06526-z |
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| author | Liwei Song |
| author_facet | Liwei Song |
| author_sort | Liwei Song |
| collection | DOAJ |
| description | Abstract With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies. |
| format | Article |
| id | doaj-art-d71f7cebb67947f98b6d1ec3e58c069a |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-d71f7cebb67947f98b6d1ec3e58c069a2025-02-02T12:36:50ZengSpringerDiscover Applied Sciences3004-92612025-01-017211410.1007/s42452-025-06526-zPerformance optimization of high-rise residential buildings in cold regions considering energy consumptionLiwei Song0Academic Affairs Office, Jilin Technology College of Electronic InformationAbstract With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies.https://doi.org/10.1007/s42452-025-06526-zEnergy consumptionHigh-rise residential buildingsGrey wolf optimization algorithmMulti-objective optimizationEnclosure structure |
| spellingShingle | Liwei Song Performance optimization of high-rise residential buildings in cold regions considering energy consumption Discover Applied Sciences Energy consumption High-rise residential buildings Grey wolf optimization algorithm Multi-objective optimization Enclosure structure |
| title | Performance optimization of high-rise residential buildings in cold regions considering energy consumption |
| title_full | Performance optimization of high-rise residential buildings in cold regions considering energy consumption |
| title_fullStr | Performance optimization of high-rise residential buildings in cold regions considering energy consumption |
| title_full_unstemmed | Performance optimization of high-rise residential buildings in cold regions considering energy consumption |
| title_short | Performance optimization of high-rise residential buildings in cold regions considering energy consumption |
| title_sort | performance optimization of high rise residential buildings in cold regions considering energy consumption |
| topic | Energy consumption High-rise residential buildings Grey wolf optimization algorithm Multi-objective optimization Enclosure structure |
| url | https://doi.org/10.1007/s42452-025-06526-z |
| work_keys_str_mv | AT liweisong performanceoptimizationofhighriseresidentialbuildingsincoldregionsconsideringenergyconsumption |