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.