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Residential Energy Management with Deep Reinforcement Learning
Wan, Zhiqiang1; Li HP(李鹤鹏)2; He, Haibo1
作者部门工业控制网络与系统研究室
会议名称2018 International Joint Conference on Neural Networks, IJCNN 2018
会议日期July 8-13, 2018
会议地点Rio de Janeiro, Brazil
会议录名称Proceedings of the International Joint Conference on Neural Networks
出版者IEEE
出版地New York
2018
页码1-7
收录类别EI
EI收录号20184706086797
产权排序2
ISBN号978-1-5090-6014-6
摘要A 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.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/23590
专题工业控制网络与系统研究室
通讯作者Wan, Zhiqiang
作者单位1.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
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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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