Residential Energy Management with Deep Reinforcement Learning

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	Residential Energy Management with Deep Reinforcement Learning
	Li HP(李鹤鹏)1 ; Wan, Zhiqiang 2; He HB(何海波)2
	2018
会议日期	July 8-13, 2018
会议地点	Rio de Janeiro, Brazil
页码	1-7
英文摘要	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.
产权排序	2
会议录	Proceedings of the International Joint Conference on Neural Networks
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5090-6014-6
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/23590]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Wan, Zhiqiang
作者单位	1.Lab. Of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, RI 02881, United States
推荐引用方式 GB/T 7714	Li HP,Wan, Zhiqiang,He HB. Residential Energy Management with Deep Reinforcement Learning[C]. 见:. Rio de Janeiro, Brazil. July 8-13, 2018.