An operational regulation method for solar heating system based on Deep reinforcement learning

The performance of solar heating systems is significantly influenced by outdoor weather fluctuations and building heating loads, leading to dynamic variations that undermine the efficacy of rule-based control (RBC) strategies. Additionally, the hydraulic and thermal time-delay characteristics frequently lead to delays in control points for real-time optimization (RTO) control strategies. While Model Predictive Control (MPC) effectively addresses these dynamic and time-delay issues in solar heating systems, its substantial computational demands limit its real-world applications. To overcome these challenges, this study proposes a Model-Free Predictive Control (MFPC) approach utilizing Deep reinforcement learning (DRL). Through TRNSYS simulations, the study conducts a comparison of the performance and energy consumption of RBC and MFPC systems, focusing on a residential solar heating system in Lhasa, Xizang as a case study. The results demonstrate that the MFPC method reduces unmet heating demand by 31% compared to traditional RBC strategies, improves solar collection efficiency by nearly 12%, and decreases tank heat loss by 2.2%. When accounting for thermal storage effects, the optimized MFPC strategy achieves a reduction in net energy consumption of 25.6%.