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 manage-ment 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.

Qian SHI;Chenyu LIU;Chao XIAO

School of Economics and Management,Tongji University,Shanghai 200092,China

工程管理前沿（英文版）

[1]Qian SHI;Chenyu LIU;Chao XIAO-.Machine learning in building energy management:A critical review and future directions)[J].工程管理前沿（英文版）,2022(02):239-256

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