Multi-objective optimization of building envelope in different climate zones in China based on BP-NSGA-Ⅱ under the future climate

Abstract

Global warming has an impact on building performance, and it is very important to explore the optimization of building performance under future climate change conditions. The study generates 2050s typical meteorological year (TMY) data of different cities (Harbin, Beijing, Shanghai, Shenzhen) representing the future climate. Taking energy consumption, thermal comfort, and initial investment cost as the objective function, the Back Propagation (BP) neural network and non-dominated sorting genetic algorithm (NSGA-Ⅱ) were used to optimize the key parameters of the building envelope of representative cities in different climate regions of China and to obtain the Pareto curve. The final solution is obtained by the weighted sum method (WSM). The results show that, except for the type of windows, the optimal configuration of the building envelope in each city is different. Compared with the results of reference buildings, the final results of each city reduces energy consumption by 14.5~24.0 % and improves thermal comfort by 23.8~34 % when the initial investment cost increases by 27.0~35.3 %. The method proposed in this paper has reference significance for the optimization of building envelope in different climatic regions of China under the future climate.