Demand-side Energy Management Method for Building Clusters Based on Reinforcement Learning

Aiming at the problems that the feasibility of reinforcement learning in demand-side energy management needs further exploration, this paper proposes a demand-side energy management method for building clusters based on reinforcement learning. Firstly, taking the building cluster as the terminal energy load carrier, the demand-side energy management framework is constructed. Secondly, based on the virtual energy storage characteristics of intelligent buildings, a novel heat resistance-capacity (R-C) balance model and user flexibility load model of intelligent buildings are constructed, and a demand-side energy management model based on reinforcement learning is constructed by combining Q-learning algorithm. Finally, through an actual simulation case, the results of demand-side energy management and the performance of the algorithm are compared and analyzed, which verifies the effectiveness and practicability of the theoretical method proposed in this paper.