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.date.updated

2021-03-30T04:08:37Z

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.identifier.issn

2076-3417

dc.identifier.uri

https://hdl.handle.net/10161/22455

dc.language

en

dc.publisher

MDPI AG

dc.relation.ispartof

Applied Sciences (Switzerland)

dc.relation.isversionof

10.3390/app11020763

dc.subject

artificial intelligence

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smart buildings

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energy efficiency

dc.title

Artificial intelligence evolution in smart buildings for energy efficiency

dc.type

Journal article

duke.contributor.orcid

Bejan, A|0000-0002-2419-2698

pubs.begin-page

1

pubs.end-page

26

pubs.issue

2

pubs.organisational-group

Pratt School of Engineering

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Mechanical Engineering and Materials Science

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Duke

pubs.publication-status

Published

pubs.volume

11

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