Temperature Control Strategy for Air Source Heat Pump Heating System Based on Deep Reinforcement Learning
The air source heat pumps(ASHP)exhibits good adjustability,and the accuracy of its modeling and the design of control strategies are key to fully exploiting its regulation potential.This paper considers the thermal storage characteristics of air source heat pump heating systems and proposes a temperature control strategy based on deep reinforcement learning(RL)for ASHP heating systems.First,a mathematical model of the ASHP heating system is established based on parameter identification.Then,a Markov decision process(MDP)model for the ASHP heating system is developed,and a temperature control strategy based on deep reinforcement learning is designed using the Q-learning algorithm.Simulation results based on real operating data demonstrate that the proposed heating system mathematical model,which accounts for heating delays,can accurately predict the variations in supply and return water temperatures as well as indoor temperatures.Furthermore,the proposed deep reinforcement learning-based temperature control strategy effectively reduces electricity costs while maintaining the indoor temperature at the set value.
air source heat pumptime of use pricingreinforcement learningwater temperature control strategy
万方数据
维普
