Data-driven Model Predictive and Reinforcement Learning-Based Control for Building Energy Management: a Survey

Huiliang Zhang, Sayani Seal, Di Wu, Francois Bouffard, Benoit Boulet
2022 IEEE Access  
Building energy management has been recognized as one of the core problems in modern power grids with respect to system operation efficiency. However, the building energy management system (BEMS) is now facing more challenges and uncertainties with the increasing penetration of renewable energy and increasing adoption of different types of electrical appliances and equipment. Classical model predictive control (MPC) has shown effective in building energy management, although it suffers from
more » ... ur-intensive modelling and complex online control optimization. Recently, with the growing accessibility to building control and automation data, data-driven solutions such as data-driven MPC and reinforcement learning (RL)-based methods have attracted more research interest. However, the potential of integrating these two types of methods and how to choose suitable control algorithms have not been well discussed. In this work, we first present a compact review of the recent advances in data-driven MPC and RL-based control methods for building energy management. Furthermore, the main challenges in these approaches and general discussions on the selection of control methods are discussed. INDEX TERMS Building energy management, model predictive control, reinforcement learning, datadriven control.
doi:10.1109/access.2022.3156581 fatcat:ql34l2572fagbdq7vxrai7aclu