| Popis: |
Accurate predictions for buildings’ energy performance (BEP) are crucial for retrofitting investment decisions and building benchmarking. With the increasing data availability and popularity of machine learning across disciplines, research started investigating machine learning for BEP predictions. While stand-alone machine learning models showed first promising results, a comprehensive analysis of advanced ensemble models to increase prediction accuracy is missing for annual BEP predictions. We implement and thoroughly tune twelve machine learning models to bridge this research gap, ranging from stand-alone to homogeneous and heterogeneous ensemble learning models. We benchmark their prediction accuracy based on an extensive real-world dataset of over 25,000 German residential buildings. The results provide strong evidence that ensemble models substantially outperform stand-alone machine learning models both on average and in the case of the best-performing model. All models are tested for robustness and systematic bias by evaluating their prediction performance along different building age classes, living space bins, and several error measures. Extreme gradient boosting as an ensemble model exhibits the highest prediction accuracy, followed by a multilayer perceptron ahead of further ensemble models. We conclude that ensemble models for annual BEP prediction are advantageous compared to stand-alone models and outperform their results in most cases. |
Full Text from ScienceDirect