Machine learning in building energy management: A critical review and future directions

Abstract

Over the past two decades, machine learning (ML) has elicited increasing attention in building energy management (BEM) research. However, the boundary of the ML-BEM research has not been clearly defined, and no thorough review of ML applications in BEM during the whole building life-cycle has been published. This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions. An integrated framework of ML-BEM, composed of four layers and a series of driving factors, is proposed. Then, based on the hype cycle model, this paper analyzes the current development status of ML-BEM and tries to predict its future development trend. Finally, five research directions are discussed: (1) the behavioral impact on BEM, (2) the integration management of renewable energy, (3) security concerns of ML-BEM, (4) extension to other building life-cycle phases, and (5) the focus on fault detection and diagnosis. The findings of this study are believed to provide useful references for future research on ML-BEM.

Graphical abstract

Keywords

building energy management / machine learning / integrated framework / knowledge evolution

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