Graph representation learning-based residential electricity behavior identification and energy management

Abstract

Abstract It is important to achieve an efficient home energy management system (HEMS) because of its role in promoting energy saving and emission reduction for end-users. Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy. However, current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications. This can lead to an insufficient description of behavior and suboptimal management strategy. To address these gaps, this paper proposes non-intrusive load monitoring (NILM) assisted graph reinforcement learning (GRL) for intelligent HEMS decision making. First, a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads. Thus, efficient identification of user behavior and description of state transition can be achieved. Second, based on the online updating of the behavior correlation graph, a GRL model is proposed to extract information contained in the graph. Thus, reliable strategy under uncertainty of environment and behavior is available. Finally, the experimental results on several datasets verify the effectiveness of the proposed model.

Keywords

• Behavior correlation graph
• Graph reinforcement learning
• Home energy management system
• Multi-label classification
• Non-intrusive load monitoring