Data-driven prediction of building energy consumption using an adaptive multi-model fusion approach

This paper develops an adaptive multi-model fusion approach to predict building energy consumption, aiming to give useful suggestions for better energy control. The building energy benchmarking dataset of Chicago in 2017 is selected as the case study, where 9 features are selected as the input variables aiming to estimate the weather normalized site energy use intensity of buildings. The training dataset is clustered using the K-means algorithm and sub-models are trained based on the clustered data using the XGBoost algorithm. The sub-models are then fused by assigning a weight considering both the model reliability and the matching degree and adopting a screening algorithm to weed out the unmatching sub-models, where the influence of the threshold in the screening algorithm is studied. The root mean square error of the estimation results from a fused model is found to be 13.42 which achieves a 7.6% amelioration compared with a single model. Moreover, the adaptive multi-model fusion approach is also proved to outperform both the two-stage clustering-based regression method and the linear fusion method. Benefiting from proper treatment of samples in the fuzzy zones between clusters and the screening algorithm in the fusion process, the method proposed in our paper eventually serves as more advanced guidance in the analysis and control of building energy performance.