Artificial intelligence evolution in smart buildings for energy efficiency

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2021-01-02

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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.

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artificial intelligence, smart buildings, energy efficiency

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Published Version (Please cite this version)

10.3390/app11020763

Publication Info

Farzaneh, H, L Malehmirchegini, A Bejan, T Afolabi, A Mulumba and PP Daka (2021). Artificial intelligence evolution in smart buildings for energy efficiency. Applied Sciences (Switzerland), 11(2). pp. 1–26. 10.3390/app11020763 Retrieved from https://hdl.handle.net/10161/22455.

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Scholars@Duke

Bejan

Adrian Bejan

J.A. Jones Distinguished Professor of Mechanical Engineering

Professor Bejan was awarded the Benjamin Franklin Medal 2018 and the Humboldt Research Award 2019. His research covers engineering science and applied physics: thermodynamics, heat transfer, convection, design, and evolution in nature.

He is ranked among the top 0.01% of the most cited and impactful world scientists (and top 10 in Engineering world wide) in the 2019 citations impact database created by Stanford University’s John Ioannidis, in PLoS Biology.  He is the author of 30 books and 700 peer-referred articles. His h-index is 111 with 92,000 citations on Google Scholar. He received 18 honorary doctorates from universities in 11 countries.


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