摘要
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%.
基金
supported by the National Natural Science Foundation of China(Project Nos.U23A20657,U20A20311).
