| Residential Energy Management with Deep Reinforcement Learning | |
Wan, Zhiqiang1; Li HP(李鹤鹏)2 ; He, Haibo1
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| Department | 工业控制网络与系统研究室 |
| Conference Name | 2018 International Joint Conference on Neural Networks, IJCNN 2018 |
| Conference Date | July 8-13, 2018 |
| Conference Place | Rio de Janeiro, Brazil |
| Source Publication | Proceedings of the International Joint Conference on Neural Networks |
| Publisher | IEEE |
| Publication Place | New York |
| 2018 | |
| Pages | 1-7 |
| Indexed By | EI |
| EI Accession number | 20184706086797 |
| Contribution Rank | 2 |
| ISBN | 978-1-5090-6014-6 |
| Abstract | 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. |
| Language | 英语 |
| Document Type | 会议论文 |
| Identifier | http://ir.sia.cn/handle/173321/23590 |
| Collection | 工业控制网络与系统研究室 |
| Corresponding Author | Wan, Zhiqiang |
| Affiliation | 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 |
| 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Residential Energy M（2794KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
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