一种基于深度学习的供热策略优化方法

Heating Strategy Optimization Method Based on Deep Learning

在中国北方,冬季楼宇集中供暖采用的策略通常为气候补偿器,但是该策略严重依赖人工经验,调节相对粗放,如何优化供热控制策略对于保持楼宇室温的稳定舒适十分重要。对此,提出了一种基于深度学习的供热策略优化方法,通过学习历史真实数据信息从而对原始控制策略进行优化。首先以学习室内温度变化的热力学规律为目标,提出了一种深度多时差分网络MTDN(Multiple Time Difference Network)来对下一时刻的室温进行预测,该网络不仅准确率高,而且符合物理规律;然后将MTDN当成模拟器,以表征人体热反应的评价指标作为相关奖励项,使用基于最大熵强化学习思想的SAC(Soft Actor Critic)算法作为策略优化器与之交互训练,从而学习到一个稳定优秀的供热控制策略;最后基于天津某个换热站的真实数据,设计相关实验分别对模拟器预测能力和策略优化器策略控制能力进行评估。验证得出:相比其他类型的预测模拟器,该模拟器不仅预测精度高,并且符合物理规律;同时,相比原始策略,该策略优化器所学的策略在随机采样的多个时段内均可以保证室内温度更加稳定舒适。

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.