Hybrid‐Driven Model‐Based Reinforcement Learning Approach for Energy Consumption Optimization of HVAC Chilled Water Systems

Heating, ventilation and air conditioning (HVAC) chilled water systems offer significant potential for energy saving and reinforcement learning (RL) methods have been extensively studied and validated for optimizing HVAC energy consumption. However, RL's low sample efficiency and reliance on randomized exploration limit its practical application. To enhance the robustness and stability of RL‐based energy optimization methods, an RL optimization approach based on a mechanism‐data hybrid‐driven model is proposed, derived from the MBPO (Model‐based policy optimization) scheme. Firstly, a novel end‐to‐end HVAC chilled water system model is developed to serve as the foundation for the hybrid model design. Second, a hybrid‐driven RL environment model framework is introduced, combining a mechanistic model with a probabilistic neural network. The mechanistic component provides generalization capabilities, while the data‐driven component offers adaptability. Third, improvements to MBPO are proposed, including double policy optimization and adaptive branch rollout, further to enhance dynamic environmental adaptability and model utilization efficiency. Finally, comparative and ablation experiments conducted using both simulation environments and measured data demonstrate that the proposed method achieves higher learning efficiency and improved robustness.