Multi-Objective Optimization of Building Envelope Retrofits Considering Future Climate Scenarios: An Integrated Approach Using Machine Learning and Climate Models
Zhikun Ding, Jinze Li, Zhan Wang, Zhaoyang Xiong
Abstract
The intensification of global climate change has exacerbated building energy consumption issues, presenting a significant challenge in retrofitting existing buildings to meet current environmental requirements while adapting to future climate scenarios. A multi-objective optimization design process for building envelope retrofits was developed in this study, utilizing random forest (RF) models and atmospheric circulation models to generate future weather data. Building Information Modeling (BIM) and performance simulations were employed to obtain data under various renovation scenarios. A BP neural network model related envelope design parameters to performance indicators, while the NSGA-III algorithm optimized retrofit strategies. The proposed method demonstrates improved efficiency and validity in developing energy-saving solutions that consider future climate scenarios. Results highlight the importance of incorporating climate change factors in retrofit designs, providing valuable insights and methodological support for decision-makers in building energy conservation practices.