TY - JOUR
T1 - Artificial intelligence evolution in smart buildings for energy efficiency
AU - Farzaneh, Hooman
AU - Malehmirchegini, Ladan
AU - Bejan, Adrian
AU - Afolabi, Taofeek
AU - Mulumba, Alphonce
AU - Daka, Precious P.
N1 - Funding Information:
Funding: This research was supported by the Kyushu Natural Energy Promotion Organization and Hitachi Global Foundation for supporting of financial support of this research.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/1/2
Y1 - 2021/1/2
N2 - 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.
AB - 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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U2 - 10.3390/app11020763
DO - 10.3390/app11020763
M3 - Review article
AN - SCOPUS:85100073812
SN - 2076-3417
VL - 11
SP - 1
EP - 26
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 2
M1 - 763
ER -