Optimization of university dormitory renovation in severe cold regions under the impact of climate change
China has many aging buildings characterized by poor thermal insulation performance and low indoor comfort levels. Climate change can impact the performance of these buildings, making climate adaptation renovations essential. This study proposes an innovative optimization framework for renovations u...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025014549 |
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| author | Anqi Wang Shuhua Yu Chunhui Shi Yanhua An |
| author_facet | Anqi Wang Shuhua Yu Chunhui Shi Yanhua An |
| author_sort | Anqi Wang |
| collection | DOAJ |
| description | China has many aging buildings characterized by poor thermal insulation performance and low indoor comfort levels. Climate change can impact the performance of these buildings, making climate adaptation renovations essential. This study proposes an innovative optimization framework for renovations under four climate scenarios, integrating BP neural networks, VIKOR, NSGA-Ⅱ, and NSGA-Ⅲ. Taking an old university dormitory in Shenyang as a case study, the research establishes a baseline model using environmental measurements and building energy consumption inventories. Two optimization algorithms perform low-dimensional (three-objective) and high-dimensional (eight-objective) optimizations. The hierarchical optimization strategy allows NSGA-Ⅱ to optimize building performance with relatively low computational resources when dealing with high-dimensional objectives. Sensitivity analysis results indicate that in severe cold regions, the importance of cooling energy consumption during summer increases as future temperatures rise. The solutions selected through hierarchical optimization can achieve energy savings of 47.9 %-54.1 % compared to the original buildings and reduce 671–762 kg of CO2 emissions per square meter during the remaining lifecycle. This framework enables the evaluation of old building performance under the influence of climate change, and its results can contribute to the research on renovation strategies for old buildings in severe cold regions. |
| format | Article |
| id | doaj-art-3d9efcfa315c4fd18e3f6cf02d6b18ea |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-3d9efcfa315c4fd18e3f6cf02d6b18ea2025-08-20T01:52:23ZengElsevierResults in Engineering2590-12302025-06-012610538410.1016/j.rineng.2025.105384Optimization of university dormitory renovation in severe cold regions under the impact of climate changeAnqi Wang0Shuhua Yu1Chunhui Shi2Yanhua An3School of Architecture, Tianjin University, Tianjin, ChinaSchool of Architecture and Planning, Shenyang Jianzhu University, Shenyang, China; Corresponding author.School of Architecture and Planning, Shenyang Urban Constucton University, Shenyang, ChinaSchool of Architecture and Planning, Shenyang Jianzhu University, Shenyang, ChinaChina has many aging buildings characterized by poor thermal insulation performance and low indoor comfort levels. Climate change can impact the performance of these buildings, making climate adaptation renovations essential. This study proposes an innovative optimization framework for renovations under four climate scenarios, integrating BP neural networks, VIKOR, NSGA-Ⅱ, and NSGA-Ⅲ. Taking an old university dormitory in Shenyang as a case study, the research establishes a baseline model using environmental measurements and building energy consumption inventories. Two optimization algorithms perform low-dimensional (three-objective) and high-dimensional (eight-objective) optimizations. The hierarchical optimization strategy allows NSGA-Ⅱ to optimize building performance with relatively low computational resources when dealing with high-dimensional objectives. Sensitivity analysis results indicate that in severe cold regions, the importance of cooling energy consumption during summer increases as future temperatures rise. The solutions selected through hierarchical optimization can achieve energy savings of 47.9 %-54.1 % compared to the original buildings and reduce 671–762 kg of CO2 emissions per square meter during the remaining lifecycle. This framework enables the evaluation of old building performance under the influence of climate change, and its results can contribute to the research on renovation strategies for old buildings in severe cold regions.http://www.sciencedirect.com/science/article/pii/S2590123025014549Climate changeBuilding energy efficiencyRetrofitting of old buildingsMulti-objective optimization |
| spellingShingle | Anqi Wang Shuhua Yu Chunhui Shi Yanhua An Optimization of university dormitory renovation in severe cold regions under the impact of climate change Results in Engineering Climate change Building energy efficiency Retrofitting of old buildings Multi-objective optimization |
| title | Optimization of university dormitory renovation in severe cold regions under the impact of climate change |
| title_full | Optimization of university dormitory renovation in severe cold regions under the impact of climate change |
| title_fullStr | Optimization of university dormitory renovation in severe cold regions under the impact of climate change |
| title_full_unstemmed | Optimization of university dormitory renovation in severe cold regions under the impact of climate change |
| title_short | Optimization of university dormitory renovation in severe cold regions under the impact of climate change |
| title_sort | optimization of university dormitory renovation in severe cold regions under the impact of climate change |
| topic | Climate change Building energy efficiency Retrofitting of old buildings Multi-objective optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025014549 |
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