Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming

doi:10.1109/TIE.2020.2978699

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 > 智能化团队

	Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming
	Wei, Qinglai 7,8,9; Liao, Zehua 7,8,9; Song, Ruizhuo 6; Zhang, Pinjia 5; Wang, Zhuo 1,2,3,4; Xiao, Jun 8
刊名	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2021-04-01
卷号	68 期号:4 页码:3599-3608
关键词	Optimal control Air conditioning Load modeling Neural networks Dynamic programming Predictive models Adaptive dynamic programming (ADP) cooling load prediction ice-storage air conditioning (IAC) neural network optimal control
ISSN号	0278-0046
DOI	10.1109/TIE.2020.2978699
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn) ; Zhang, Pinjia(pinjia.zhang@ieee.org) ; Xiao, Jun(xiaojun@ucas.ac.cn)
英文摘要	In this article, the optimal control scheme for ice-storage air conditioning (IAC) system is solved via a data-based adaptive dynamic programming (ADP) method. It is the first time that ADP is employed to design a self-learning scheme, which obtains the optimal control policy of IAC system. First, based on the data of the temperature, irradiance, and cooling load in an actual project, a prediction model of cooling load is built by a three-layer neural network with the performance verification. Second, the operation of the IAC system is analyzed. Third, a data-based ADP method is designed to realize a self-learning optimal control for the IAC system. Then, numerical results show that using the data-based optimal control method can reduce the operation costs. Finally, the comparison results show that the developed ADP method improves the system efficiency, minimizing the overall cost. Thus, the superiority of the developed algorithm is verified.
资助项目	National Natural Science Foundation of China[51822705] ; National Natural Science Foundation of China[61873300] ; National Natural Science Foundation of China[61722312] ; National Natural Science Foundation of China[61673041] ; National Natural Science Foundation of China[61533017] ; Fundamental Research Funds for the Central Universities[FRF-BD-19-002 A] ; Fundamental Research Funds for the Central Universities[Y18G34]
WOS关键词	LOAD PREDICTION ; PERFORMANCE ; NETWORKS
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000599525100079
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42746]
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
通讯作者	Wei, Qinglai; Zhang, Pinjia; Xiao, Jun
作者单位	1.Beihang Univ, Key Lab Minist Ind & Informat Technol Quantum Sen, Beijing 100191, Peoples R China 2.Beijing Acad Quantum Informat Sci, Beijing 100193, Peoples R China 3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China 4.Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China 5.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China 6.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 7.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 9.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wei, Qinglai,Liao, Zehua,Song, Ruizhuo,et al. Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2021,68(4):3599-3608.
APA	Wei, Qinglai,Liao, Zehua,Song, Ruizhuo,Zhang, Pinjia,Wang, Zhuo,&Xiao, Jun.(2021).Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,68(4),3599-3608.
MLA	Wei, Qinglai,et al."Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68.4(2021):3599-3608.