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Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization

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  • Zhou, Yuekuan
  • Zheng, Siqian

Abstract

Integrating advanced materials in building glazing systems is critical for promoting net-zero energy buildings. In this research, both experimental and numerical studies were conducted on an aerogel glazing system. In order to provide climate adaptive designs on the aerogel glazing system with optimal geometric and operating parameters, a generic optimization methodology was developed by flexibly integrating supervised machine learning and advanced teaching-learning-based optimization algorithm. The proposed optimization methodology was thereafter used for optimal system designs in different climate regions. Results indicate that the proposed surrogate model can intelligently and accurately learn and update the optimization function with straightforward mathematical associations between multivariables and objectives. In addition, within optimal cases, total heat gain and heat flux are dominated by the extinction coefficient in southern cities, whereas the total heat gain is dominated by the thermal conductivity in the northern city, LanZhou. By adopting the proposed technique in this study, compared to optimal results following the Taguchi standard orthogonal array, the total heat gain can be reduced by 62.5% to 36.27 kWh/m2 in LanZhou, and by 5.9% to 267.18 kWh/m2 in GuangZhou, respectively. This study formulates a general methodology for climate adaptive optimal designs on aerogel glazing systems in different climatic regions.

Suggested Citation

  • Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.
    • Handle: RePEc:eee:renene:v:153:y:2020:i:c:p:375-391
    DOI: 10.1016/j.renene.2020.01.133
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    References listed on IDEAS

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    3. Xie, Xing & Chen, Xing-ni & Xu, Bin & Fei, Yue & Pei, Gang, 2022. "Study based on “Heat Flux - Energy Saving Pointer”: Exploring why phase change materials is not energy efficient enough on internal wall in cold region," Renewable Energy, Elsevier, vol. 196(C), pages 1308-1324.
    4. Zhou, Yuekuan & Zheng, Siqian & Liu, Zhengxuan & Wen, Tao & Ding, Zhixiong & Yan, Jun & Zhang, Guoqiang, 2020. "Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optim," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    5. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    6. Liu, Zhengxuan & Sun, Pengchen & Xie, Mingjing & Zhou, Yuekuan & He, Yingdong & Zhang, Guoqiang & Chen, Dachuan & Li, Shuisheng & Yan, Zhongjun & Qin, Di, 2021. "Multivariant optimization and sensitivity analysis of an experimental vertical earth-to-air heat exchanger system integrating phase change material with Taguchi method," Renewable Energy, Elsevier, vol. 173(C), pages 401-414.
    7. Zhou, Yuekuan & Zheng, Siqian, 2020. "Stochastic uncertainty-based optimisation on an aerogel glazing building in China using supervised learning surrogate model and a heuristic optimisation algorithm," Renewable Energy, Elsevier, vol. 155(C), pages 810-826.
    8. Zhou, Yuekuan & Zheng, Siqian, 2024. "A co-simulated material-component-system-district framework for climate-adaption and sustainability transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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