Reinforcement learning for building controls: The opportunities and challenges

• Published in: Applied energy Vol. 269; no. C; p. 115036
• Main Authors: Wang, Zhe, Hong, Tianzhen
• Format: Journal Article
• Language: English
• Published: United Kingdom Elsevier Ltd 01.07.2020; Elsevier
• Subjects: algorithms; artificial intelligence; Building controls; Building performance; buildings; data collection; energy; learning; Machine learning; Optimization; Reinforcement learning; Building performance; Optimization; Building controls; Reinforcement learning; Machine learning
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2020.115036

•Reinforcement learning has been explored for building control applications.•We reviewed studies using reinforcement learning for building controls.•We surveyed algorithm, state, action, reward, and the environment for reinforcement learning controller.•Research trends, progress and gaps of this field have been identified.•Adoption of reinforcement learning based controls in real buildings still faces significant challenges. Building controls are becoming more important and complicated due to the dynamic and stochastic energy demand, on-site intermittent energy supply, as well as energy storage, making it difficult for them to be optimized by conventional control techniques. Reinforcement Learning (RL), as an emerging control technique, has attracted growing research interest and demonstrated its potential to enhance building performance while addressing some limitations of other advanced control techniques, such as model predictive control. This study conducted a comprehensive review of existing studies that applied RL for building controls. It provided a detailed breakdown of the existing RL studies that use a specific variation of each major component of the Reinforcement Learning: algorithm, state, action, reward, and environment. We found RL for building controls is still in the research stage with limited applications (11%) in real buildings. Three significant barriers prevent the adoption of RL controllers in actual building controls: (1) the training process is time consuming and data demanding, (2) the control security and robustness need to be enhanced, and (3) the generalization capabilities of RL controllers need to be improved using approaches such as transfer learning. Future research may focus on developing RL controllers that could be used in real buildings, addressing current RL challenges, such as accelerating training and enhancing control robustness, as well as developing an open-source testbed and dataset for performance benchmarking of RL controllers.