Model-free optimization for air conditioning cooling water systems based on deep Q network (DQN)

 Model-based control methods have been widely investigated and validated in the domain of optimal control for building air conditioning water systems. However, the performance of model-based control highly depends on accurate system models and enough sensors, which are difficult to obtain in some buildings. To overcome this problem, a model-free optimization method for air conditioning cooling water system based on deep Q network (DQN) is proposed. The wet bulb temperature of outdoor air, system cooling load and chiller on/off states are taken as the states, the frequencies of cooling tower fans and cooling water pumps are taken as the actions, and the reward is the system COP. In the simulation environment built by the measured data of an actual system, the model optimization method based on particle swarm optimization, the reinforcement learning method based on Q-value (Q learning) optimization and the model-free optimization method based on DQN are used to conduct experiment. The results show that the model-free optimization method based on DQN has the best optimization effect with 7.68% average COP improvement and 7.15% energy saving rate, which has a better energy saving effect in complex systems.