Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy
Shi, Guang1; Liu, Derong2; Wei, Qinglai1
2017-04-25
发表期刊IET CONTROL THEORY AND APPLICATIONS
卷号11期号:7页码:915-922
文章类型Article
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.
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
WOS标题词Science & Technology ; Technology
DOI10.1049/iet-cta.2016.0653
关键词[WOS]TIME NONLINEAR-SYSTEMS ; NEURAL-NETWORK ; SPEECH RECOGNITION ; MANAGEMENT-SYSTEM ; PREDICTION ; SCHEME ; SERIES ; MODEL
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61233001 ; 61273140 ; 61374105 ; 61533017 ; U1501251)
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号WOS:000399568800003
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/13635
专题复杂系统管理与控制国家重点实验室_平行控制
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
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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.
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