Residential Energy Management with Deep Reinforcement Learning | |
Wan, Zhiqiang1; Li HP(李鹤鹏)2![]() | |
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. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Residential Energy M(2794KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论