Research on central air conditioning demand response optimization based on DDPG

This paper proposes a central air-conditioning demand response optimization method based on the deep deterministic policy gradient (DDPG) algorithm. By constructing a Markov decision model, combining the energy consumption prediction mechanism with the multi-agent control framework, the adaptive identification of the system state and the generation of dynamic control strategies are realized. The experience replay and target network soft update mechanism are used in the model training process to effectively improve the convergence stability. The simulation experiments conducted in the office building environment of the Institute of Electrical Engineering show that this method significantly reduces the system energy consumption while maintaining indoor comfort, and has strong response speed and strategy generalization ability. Compared with traditional methods such as PID and MPC, DDPG shows better overall performance, which verifies its application potential in energy efficiency management of intelligent buildings.