Building Energy Management With Reinforcement Learning and Model Predictive Control: A Survey

Building energy management has been recognized as of significant importance on improving the overall system efficiency and reducing the greenhouse gas emission. However, the building energy management system 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 labour-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.

Automated summary

Building energy management is crucial for improving system efficiency and reducing greenhouse gas emissions. The challenges and uncertainties in the field have increased with the rise of renewable energy and diverse electrical appliances. While classical model predictive control has been effective, data-driven solutions like data-driven MPC and reinforcement learning-based methods have gained research interest. However, the integration of these methods and the selection of suitable control algorithms require further discussion.