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Machine learning for smart building applications: Review and taxonomy

Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko

Authors

Roufaida Laidi

Youcef Djenouri

Ilangko Balasingham



Abstract

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.

Journal Article Type Article
Publication Date May 1, 2019
Journal ACM Computing Surveys
Print ISSN 0360-0300
Electronic ISSN 1557-7341
Publisher Association for Computing Machinery
Peer Reviewed Peer Reviewed
Volume 52
Issue 2
Pages 1-36
APA6 Citation Djenouri, D., Laidi, R., Djenouri, Y., & Balasingham, I. (2019). Machine learning for smart building applications: Review and taxonomy. ACM Computing Surveys, 52(2), 1-36. https://doi.org/10.1145/3311950
DOI https://doi.org/10.1145/3311950

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Copyright Statement
© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys (CSUR) Vol. 52, No. 2 (2019) http://doi.acm.org/10.1145/3311950





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