Advanced metering infrastructure and bilateral communication technologies facilitate the development of the home energy management system in the smart home. In this paper, we propose an energy management strategy for controllable loads based on reinforcement learning (RL). First, based on the mathematical model, the Markov decision process of different types of home energy resources (HERs) is formulated. Then, two RL algorithms, i.e. deep Q-learning and deep deterministic policy gradient are utilized. Based on the living habits of the residents, the dependency modes for HERs are proposed and are integrated into the reinforcement learning algorithms. Through the case studies, it is verified that the proposed method can schedule HERs properly to satisfy the established dependency modes. The difference between the achieved result and the optimal solution is relatively small.

先进的计量基础设施和双边通信技术促进了智能家居中家庭能源管理系统的发展。在本文中，我们提出了一种基于强化学习（RL）的可控负荷能量管理策略。首先，基于数学模型，制定了不同类型家庭能源（HERs）的马尔可夫决策过程。然后，利用了两种RL算法，即深度Q学习和深度确定性策略梯度。根据居民的生活习惯，提出了HER的依赖模式，并将其整合到强化学习算法中。通过案例研究，验证了该方法能够正确调度HERs以满足已建立的依赖模式。