Abstract: Typically, the strategy of central heating for buildings in winter is climate compensator.However, this strategy heavily relies on manual experience with a relatively simple regulation.Therefore, how to optimize the heating control strategy is very important to keep the indoor temperature stable and comfortable.For this task, this paper proposes a heating strategy optimization method based on deep learning and deep reinforcement learning, which can optimize the original control strategy based on real historical data.The paper first develops a deep MTDN (Multiple Time Difference Network) as the simulator to predict the next time slot's room temperature.By learning the thermodynamic law of indoor temperature change, the network has high accuracy and confirms the physical laws.After that, the SAC (Soft Actor-Critic) algorithm based on maximum entropy reinforcement learning is employed as the strategy optimizer to interact with the simulator.Here, we use the evaluation index of the human body's thermal response as the reward to train and optimize the heating control strategy.Based on the real data of a heat exchange station in Tianjin, we evaluate the predictive ability of the simulator and the control ability of the strategy optimizer, respectively.The results verify that, compared with other types of prediction simulators, this simulator not only has high prediction accuracy but also conforms to physical laws.At the same time, compared with the original strategy, the strategy learned by the strategy optimizer can ensure that the indoor temperature is more stable and comfortable in multiple time periods of random sampling.

Key words: Central heating, Deep learning, Deep reinforcement learning, Heating optimization, Urban computing