A review of reinforcement learning for autonomous building energy management. (September 2019)

• Record Type: Journal Article
• Authors: Mason, Karl; Grijalva, Santiago
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 300
• Page End: 312
• Publication Date: 2019-09
• Subjects: Reinforcement learning -- Building energy management -- Smart homes -- Smart grid -- Deep learning -- Machine learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.07.019
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. Energy savings of greater than 20% are reported in the literature for more complex building energy management problems when implementing reinforcement learning. The main direction for future research and challenges in reinforcement learning are also outlined.