Interpretable machine learning for building energy management: A state-of-the-art review

Abstract

Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to the ever-increasing availability of massive building operational data. However, it is challenging for end-users to understand and trust machine learning models because of their black-box nature. To this end, the interpretability of machine learning models has attracted increasing attention in recent studies because it helps users understand the decisions made by these models. This article reviews previous studies that adopted interpretable machine learning techniques for building energy management to analyze how model interpretability is improved. First, the studies are categorized according to the application stages of interpretable machine learning techniques: ante-hoc and post-hoc approaches. Then, the studies are analyzed in detail according to specific techniques with critical comparisons. Through the review, we find that the broad application of interpretable machine learning in building energy management faces the following significant challenges: (1) different terminologies are used to describe model interpretability which could cause confusion, (2) performance of interpretable ML in different tasks is difficult to compare, and (3) current prevalent techniques such as SHAP and LIME can only provide limited interpretability. Finally, we discuss the future R&D needs for improving the interpretability of black-box models that could be significant to accelerate the application of machine learning for building energy management.