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

• Published in: Renewable energy Vol. 153; pp. 375 - 391
• Main Authors: Zhou, Yuekuan, Zheng, Siqian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2020
• Subjects: Aerogel glazing system; aerogels; algorithms; artificial intelligence; buildings; cities; Climatic regions; climatic zones; energy; heat transfer; Machine learning; Optimization function; renewable energy sources; systems engineering; Teaching-learning-based optimization; thermal conductivity; Climatic regions; Optimization function; Aerogel glazing system; Machine learning; Teaching-learning-based optimization
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2020.01.133

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. •Granular aerogels were applied to form a new translucent window.•Tradeoff was investigated between heat transfer and solar radiation transmission.•A flexible optimization method was developed using machine learning method.•Optimal design of aerogel glazing system was through multivariable optimization.•Climate adaptive optimal design was studied using an advanced algorithm.