Improving Site Energy Use Intensity Analysis: A Multi-Level Data-Driven Approach

This study investigates the effectiveness of common thermal, climate, and envelope features in predicting annual site energy use intensity (site EUI) for different types of residential buildings in the USA. A proposed multi-level data approach that consists of regression algorithms and feature analysis has been implemented to derive models from different sets of features related to thermal, envelope, and climate, respectively. Feature set analysis is conducted using correlation analysis methods besides chi-square testing (CHI) and gain ratio (GR) methods to offer interpretable global features rankings. Models were developed using regression-based algorithms (linear, lasso, and ridge) under a 10-fold cross-validation on different distinct sets of features besides permutation feature importance (PFI) analyses to validate the models in terms of root mean squared error (RMSE). The novelty of this study lies in the comparison of feature groups and the evaluation of their individual and incremental contributions to site EUI prediction. Results against the WiDS Datathon 2022 building energy dataset demonstrate consistently ranked climate and thermal indicators (accumulated annual heating degree days (AAH) and accumulated annual cooling degree days (AAC), and heating dominance (HD), cooling dominance (CD), snowfall, and extreme temperature days) as the most informative predictors among the evaluated feature groups. The model with the best performance has an RMSE value of about 38.68; however, from the low Coefficient of determination (R2) values, it can be noted that yearly climatic conditions and building envelope characteristics cannot be only used to account for the variation in site EUIs on their own, thus showing the need to consider other factors.