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Artificial intelligence evolution in smart buildings for energy efficiency

dc.contributor.author Farzaneh, H
dc.contributor.author Malehmirchegini, L
dc.contributor.author Bejan, A
dc.contributor.author Afolabi, T
dc.contributor.author Mulumba, A
dc.contributor.author Daka, PP
dc.date.accessioned 2021-03-30T04:08:40Z
dc.date.available 2021-03-30T04:08:40Z
dc.date.issued 2021-01-02
dc.identifier.issn 2076-3417
dc.identifier.uri https://hdl.handle.net/10161/22455
dc.description.abstract © 2021 by the authors. Licensee MDPI, Basel, Switzerland. The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in‐depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI‐based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings.
dc.language en
dc.publisher MDPI AG
dc.relation.ispartof Applied Sciences (Switzerland)
dc.relation.isversionof 10.3390/app11020763
dc.subject artificial intelligence
dc.subject smart buildings
dc.subject energy efficiency
dc.title Artificial intelligence evolution in smart buildings for energy efficiency
dc.type Journal article
duke.contributor.id Bejan, A|0095916
dc.date.updated 2021-03-30T04:08:37Z
pubs.begin-page 1
pubs.end-page 26
pubs.issue 2
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Mechanical Engineering and Materials Science
pubs.organisational-group Duke
pubs.publication-status Published
pubs.volume 11
duke.contributor.orcid Bejan, A|0000-0002-2419-2698


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