Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches
研究使用六种AI机器学习算法预测和规范建筑翻新后的能源性能，发现梯度提升模型预测准确率达84.34%，并识别出高效供暖系统、优化住宅特征等关键节能策略。
The energy performance (EP) of buildings is critical for European governments to meet their decarbonization targets by 2050. In the context of European Union (EU) policies, which subsidize citizen-led building renovations, it is imperative to ascertain the efficacy of these renovations in significantly enhancing EP. This study relies on six AI-based machine learning (ML) algorithms to identify key predictors and prescribe measures for enhancing post-renovation EP in building refurbishments. The gradient boosting model outperforms the other ML models with an accuracy rate of 84.34 % as the most effective predictive model. Moreover, an analysis of numerous predictors in the experiment showed that implementing modern energy-efficient heating systems , optimizing dwelling characteristics, regular maintenance, investing in high-performance insulation materials, and understanding the dynamics of the occupants were relevant prescriptions for efficient energy-saving strategies. The results should enable market actors to make optimal decisions regarding EP refurbishments.