Residential Energy Management with Deep Reinforcement Learning

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	Residential Energy Management with Deep Reinforcement Learning
	Wan, Zhiqiang 1; Li HP(李鹤鹏)2 ; He, Haibo 1
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
推荐引用方式 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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