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Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

doi: 10.3390/app11020763
handle: 10161/22455
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.
- Kyushu University Japan
- Duke University United States
- Kyushu University Japan
690, Technology, QH301-705.5, T, Physics, QC1-999, artificial intelligence, Engineering (General). Civil engineering (General), Chemistry, smart buildings, TA1-2040, Biology (General), QD1-999, energy efficiency
690, Technology, QH301-705.5, T, Physics, QC1-999, artificial intelligence, Engineering (General). Civil engineering (General), Chemistry, smart buildings, TA1-2040, Biology (General), QD1-999, energy efficiency
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).163 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1%
