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

doi:10.1049/iet-cta.2016.0653

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Echo state network-based Q-learning method for optimal battery control of offices combined with renewable energy
	Shi, Guang1 ; Liu, Derong 2; Wei, Qinglai1
发表期刊	IET CONTROL THEORY AND APPLICATIONS
	2017-04-25
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
DOI	10.1049/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：30[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	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
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
第一作者单位	中国科学院自动化研究所
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.

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