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

文献类型：期刊论文

作者Wei, Qinglai2,3,4; Liao, Zehua2,3,4; Song, Ruizhuo5; Zhang, Pinjia1; Wang, Zhuo6,7,8,9; Xiao, Jun3
刊名IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
出版日期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
DOI10.1109/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.
WOS关键词LOAD PREDICTION ; PERFORMANCE ; NETWORKS
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研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000599525100079
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
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.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
5.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
6.Beihang Univ, Res Inst Frontier Sci, Beijing 100191, Peoples R China
7.Beihang Univ, Key Lab Minist Ind & Informat Technol Quantum Sen, Beijing 100191, Peoples R China
8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
9.Beijing Acad Quantum Informat Sci, Beijing 100193, 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.

入库方式： OAI收割

来源：自动化研究所

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