Reinforcement learning for building controls: The opportunities and challenges

By Zhe Wang, Tianzhen Hong. Applied Energy, 2020

513 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/37593c3803fce4a197ada24180dfe56cd62ea50d

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Citations in the corpus

Applications of reinforcement learning in energy systems (2021, background): “Recently [28], reviewed the recent progress in building energy management systems using the same approach.”
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives (2022, methodology): “…value function approximation approaches (e.g., coarse coding, ML algorithms) are used; and (ii) deep RL (DRL), which represents the evolution of the traditional methods where DL models (e.g., deep NNs, CNNs, RNNs) are used to approximate the state and/or action value (Wang and Hong 2020).”
A Review of Deep Reinforcement Learning for Smart Building Energy Management (2020, methodology): “Although there are several surveys on building energy systems, the involved methods are RL [57]–[60] or other artificial intelligence methods (e.”