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

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…

• MO
  Ministry of Science and Technology of the People's Republic of China
• RG
  Research Grants Council, University Grants Committee

  Award: C5018–20GF
• NK
  National Key Research and Development Program of China

  Award: 2021YFE0107400