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

Protection and Control of Modern Power Systems

Original Research

Shengchun Yang¹  Zhenning Pan²  Xinpei Chen²  Zihao Wang²  Tao Yu² 

[1] China Electric Power Research Institute, 210003, Nanjing, China;School of Electric Power Engineering, South China University of Technology, 510640, Guangzhou, China;

关键词: Behavior correlation graph;    Graph reinforcement learning;    Home energy management system;    Multi-label classification;    Non-intrusive load monitoring;   

DOI  :  10.1186/s41601-023-00305-x

 received in 2022-12-23, accepted in 2023-06-20,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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