Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation

Abstract

As the dual carbon goals are being approached, there has been an increase in the number of energy-saving renovation projects for existing buildings. However, building renovation also brings about environmental impacts and incremental costs, which need to be addressed urgently. This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. To enhance the robustness of the methodology, a comparative analysis of four different ML models—light gradient boosting machine (LightGBM), fast random forest (FRF), multivariate linear regression (MVLR), and artificial neural network (ANN)—was conducted, with LightGBM demonstrating the best performance in terms of accuracy, adaptability, and efficiency. Using the heuristic optimization algorithm and entropy-weighted method, the framework achieved average energy savings of 56.62%, a reduction in carbon emissions of 51.60%, and a 24.27% decrease in life-cycle costs. Compared to local ultra-low-energy building standards, the optimal solutions resulted in a 2.60% reduction in carbon emissions and a 15.85% decrease in life-cycle costs. This integrated framework demonstrates the potential of combining machine learning surrogate models, evolutionary generation, and entropy-weighted methods in building energy retrofitting optimizations, offering a novel, efficient, and adaptable approach for researchers and practitioners seeking to balance energy consumption, carbon emissions, and life-cycle costs in renovation projects.

Topics & Concepts