Identifying the optimal heterogeneous ensemble learning model for building energy prediction using the exhaustive search method

Abstract

Heterogeneous ensemble learning has received increasing attention in building energy prediction research because it provides more stable and accurate predictions than the conventional single model-based methods. Although existing studies have proved the feasibility of adopting heterogeneous ensemble learning in building energy prediction, they have not fully explored the method of constructing an optimal heterogeneous ensemble learning model based on limited base model resources. This paper attempts to identify the optimal heterogeneous ensemble learning model for building energy prediction by using the exhaustive search method. Six machine learning methods are used as the alternative base model for constructing heterogeneous ensemble learning models. All 57 heterogeneous ensemble learning models that can be built from the six base models are compared and the optimal model is identified. The hourly building energy prediction experiment is performed on an institutional building located at the University of Florida to verify the proposed exhaustive search method. Three modules representing different semesters of the test building are developed and tested to examine the predictive performance of the proposed heterogeneous ensemble learning models. The research findings demonstrate the feasibility of the proposed exhaustive search method in identifying the optimal heterogeneous ensemble learning model. The optimal heterogeneous ensemble learning model found in this study can reduce the MAPE by 4.9% − 6.6% compared with its most accurate base model in the model testing stage. The experimental results and research findings provide critical guidance for applying heterogeneous ensemble learning to data-driven building energy prediction.