Abstract
Improving energy efficiency and reducing environmental impact are among the most significant global challenges today. In particular, building energy systems (BESs) are the main consumers of urban energy. Reducing energy consumption and optimizing the operation of BESs can help conserve energy resources. Traditional rule-based controllers (RBCs) are limited in their ability to optimize building energy consumption while maintaining occupant comfort. Data-driven model predictive control (MPC), which uses building thermal dynamic models and historical data for training, may struggle to handle uncertain cases. Reinforcement learning (RL) allows the control systems to make decisions in real time. However, when the system becomes scalable, training a single agent becomes complicated and more time-consuming. Therefore, multi-agent reinforcement learning (MARL) methods are being investigated and applied to building-related energy systems. This paper provides a comprehensive literature review of recent advancements in MARL control methods for building-related energy systems and evaluates their adaptability, complexity, and realization. This review paper identifies current challenges and future directions in this field.