The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand
Open accessAbstract
The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of: 1) setting the model for multi-objective optimization; 2) sensitivity analysis for reducing the dimension of input variables, and; 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay’s theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice.
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Cites
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Cited by
Halhoul Merabet, G., Essaaidi, M., Ben Haddou, M., Qolomany, B., Qadir, J., Anan, M., Al-Fuqaha, A., Abid, M. R., & Benhaddou, D. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110969 · Cited by 422
Tushar, Q., Bhuiyan, M. A., Zhang, G., & Maqsood, T. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.125622 · Cited by 152
AI-driven design optimization for sustainable buildings: a systematic review
Manmatharasan, P., Bitsuamlak, G., & Grolinger, K. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115440 · Cited by 63
Rahif, R., Kazemi, M., & Attia, S. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112998 · Cited by 51
Yu, C.-R., Guo, H.-S., Wang, Q.-C., & Chang, R.-D. (2020) · Applied Sciences · doi:10.3390/app10124188 · Cited by 50
Metadata
- Authors
- Gou, S., Nik, V. M., Scartezzini, J.-L., Zhao, Q., & Li, Z.
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 169
- Pages
- 484-506
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W2762147537
- Cited by
- 313
- References
- 32
- FWCI
- 18.33
- Type
- journal article
- Language
- en
- Open access
- green
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- Gou, S., Nik, V. M., Scartezzini, J.-L., Zhao, Q., & Li, Z. (2018). Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2017.09.095
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