Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy

doi:10.1049/iet-cta.2016.0653

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 平行控制

	Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy
	Shi, Guang1 ; Liu, Derong 2; Wei, Qinglai1
刊名	IET CONTROL THEORY AND APPLICATIONS
	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)
内容类型	期刊论文
源URL	[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
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.