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

doi:10.1007/s42524-021-0181-1

Front. Eng

2022, Vol. 9

Issue (2) : 239-256 https://doi.org/10.1007/s42524-021-0181-1

REVIEW ARTICLE

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

Qian SHI, Chenyu LIU, Chao XIAO(

)

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

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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.

Keywords building energy management machine learning integrated framework knowledge evolution

Corresponding Author(s): Chao XIAO

Just Accepted Date: 29 November 2021 Online First Date: 10 January 2022 Issue Date: 25 May 2022

Cite this article:

Qian SHI,Chenyu LIU,Chao XIAO. Machine learning in building energy management: A critical review and future directions[J]. Front. Eng, 2022, 9(2): 239-256.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0181-1
https://academic.hep.com.cn/fem/EN/Y2022/V9/I2/239

Fig.1 Research processes of this study.

Tab.1 Keywords for searching

Fig.2 Paper co-citation network for the ML-BEM: 1998–2020.

First author	Title	Year	Freq.	Journal
Zhao HX	A review on the prediction of building energy consumption	2012	44	Renewable & Sustainable Energy Reviews
Fan C	Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques	2014	23	Applied Energy
Jain RK	Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy	2014	21	Applied Energy
Fan C	A short-term building cooling load prediction method using deep learning algorithms	2017	20	Applied Energy
Pedregosa F	Scikit-learn: Machine learning in Python	2011	17	Journal of Machine Learning Research
Ahmad AS	A review on applications of ANN and SVM for building electrical energy consumption forecasting	2014	16	Renewable & Sustainable Energy Reviews
Edwards RE	Predicting future hourly residential electrical consumption: A machine learning case study	2012	16	Energy and Buildings
Amasyali K	A review of data-driven building energy consumption prediction studies	2018	15	Renewable & Sustainable Energy Reviews
Foucquier A	State of the art in building modelling and energy performances prediction: A review	2013	13	Renewable & Sustainable Energy Reviews
Wei YX	A review of data-driven approaches for prediction and classification of building energy consumption	2018	12	Renewable & Sustainable Energy Reviews
Robinson C	Machine learning approaches for estimating commercial building energy consumption	2017	12	Applied Energy
Tsanas A	Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools	2012	11	Energy and Buildings
Li Q	Applying support vector machine to predict hourly cooling load in the building	2009	11	Applied Energy
Chae YT	Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings	2016	10	Energy and Buildings

Tab.2 Papers in the ML-BEM field with co-citation frequency more than 10

Fig.3 Keywords co-occurrence network of ML-BEM from 1998 to 2020.

Tab.3 The most commonly used ML techniques and hottest BEM topics

Fig.4 Cluster analysis in the ML-BEM field: 1998–2020.

Tab.4 Seven research clusters in the field of ML-BEM

Fig.5 The comprehensive framework.

Tab.5 Most frequently used ML-related method

Tab.6 Proportion of papers studying different building types

Fig.6 The knowledge evolution of ML-BEM.

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