Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy
文献类型：期刊论文
| 作者 | Shi, Guang1 ; Liu, Derong2; Wei, Qinglai1
|
| 刊名 | IET CONTROL THEORY AND APPLICATIONS
 |
| 出版日期 | 2017-04-25 |
| 卷号 | 11期号:7页码:915-922 |
| 关键词 | Recurrent Neural Nets Neurocontrollers Learning (Artificial Intelligence) Office Environment Optimal Control Solar Power Energy Consumption Time Series Secondary Cells Energy Management Systems Function Approximation Echo State Network-based Q-learning Method Optimal Battery Control Renewable Energy Optimal Energy Management Solar Energy Energy Consumption Energy Demand Time Series Real-time Electricity Rate Periodic Functions Q-function Optimal Charging Strategy Optimal Discharging Strategy Optimal Idle Strategy Numerical Analysis |
| DOI | 10.1049/iet-cta.2016.0653 |
| 文献子类 | Article |
| 英文摘要 | An echo state network (ESN)-based Q-learning method is developed for optimal energy management of an office, where the solar energy is introduced as the renewable source, and a battery is installed with a control unit. The energy consumption in the office, also considered as the energy demand, is separated into those from sockets, lights and air-conditioners. First, ESNs, well known for their excellent modelling performance for time series, are employed to model the time series of the real-time electricity rate, renewable energy and energy demand as periodic functions. Second, given the periodic models of the electricity rate, renewable energy and energy demand, an ESN-based Q-learning method with the Q-function approximated by an ESN is developed and implemented to determine the optimal charging/discharging/idle strategies for the battery in the office, so that the total cost of electricity from the grid can be reduced. Finally, numerical analysis is conducted to illustrate the performance of the developed method. |
| WOS关键词 | TIME NONLINEAR-SYSTEMS ; NEURAL-NETWORK ; SPEECH RECOGNITION ; MANAGEMENT-SYSTEM ; PREDICTION ; SCHEME ; SERIES ; MODEL |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000399568800003 |
| 资助机构 | National Natural Science Foundation of China(61233001 ; 61273140 ; 61374105 ; 61533017 ; U1501251) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13635]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Guang,Liu, Derong,Wei, Qinglai. Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy[J]. IET CONTROL THEORY AND APPLICATIONS,2017,11(7):915-922. |
| APA | Shi, Guang,Liu, Derong,&Wei, Qinglai.(2017).Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy.IET CONTROL THEORY AND APPLICATIONS,11(7),915-922. |
| MLA | Shi, Guang,et al."Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy".IET CONTROL THEORY AND APPLICATIONS 11.7(2017):915-922. |
入库方式： OAI收割
来源：自动化研究所
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