SIA OpenIR  > 工业控制网络与系统研究室
Residential Energy Management with Deep Reinforcement Learning
Wan, Zhiqiang1; Li HP(李鹤鹏)2; He, Haibo1
Department工业控制网络与系统研究室
Conference Name2018 International Joint Conference on Neural Networks, IJCNN 2018
Conference DateJuly 8-13, 2018
Conference PlaceRio de Janeiro, Brazil
Source PublicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Publication PlaceNew York
2018
Pages1-7
Indexed ByEI
EI Accession number20184706086797
Contribution Rank2
ISBN978-1-5090-6014-6
AbstractA smart home with battery energy storage can take part in the demand response program. With proper energy management, consumers can purchase more energy at off-peak hours than at on-peak hours, which can reduce the electricity costs and help to balance the electricity demand and supply. However, it is hard to determine an optimal energy management strategy because of the uncertainty of the electricity consumption and the real-time electricity price. In this paper, a deep reinforcement learning based approach has been proposed to solve this residential energy management problem. The proposed approach does not require any knowledge about the uncertainty and can directly learn the optimal energy management strategy based on reinforcement learning. Simulation results demonstrate the effectiveness of the proposed approach.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23590
Collection工业控制网络与系统研究室
Corresponding AuthorWan, Zhiqiang
Affiliation1.Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, RI 02881, United States
2.Lab. Of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Wan, Zhiqiang,Li HP,He, Haibo. Residential Energy Management with Deep Reinforcement Learning[C]. New York:IEEE,2018:1-7.
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