Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming
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, b...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 2021-04, Vol.68 (4), p.3599-3608 |
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| container_title | IEEE transactions on industrial electronics (1982) |
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| creator | Wei, Qinglai Liao, Zehua Song, Ruizhuo Zhang, Pinjia Wang, Zhuo Xiao, Jun |
| description | 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. |
| doi_str_mv | 10.1109/TIE.2020.2978699 |
| format | Article |
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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.</description><identifier>ISSN: 0278-0046</identifier><identifier>EISSN: 1557-9948</identifier><identifier>DOI: 10.1109/TIE.2020.2978699</identifier><identifier>CODEN: ITIED6</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive control ; Adaptive dynamic programming (ADP) ; Air conditioning ; Algorithms ; Control methods ; cooling load prediction ; Cooling loads ; Dynamic programming ; ice-storage air conditioning (IAC) ; Irradiance ; Learning ; Load modeling ; neural network ; Neural networks ; Optimal control ; Prediction models ; Predictive models</subject><ispartof>IEEE transactions on industrial electronics (1982), 2021-04, Vol.68 (4), p.3599-3608</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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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.</description><subject>Adaptive control</subject><subject>Adaptive dynamic programming (ADP)</subject><subject>Air conditioning</subject><subject>Algorithms</subject><subject>Control methods</subject><subject>cooling load prediction</subject><subject>Cooling loads</subject><subject>Dynamic programming</subject><subject>ice-storage air conditioning (IAC)</subject><subject>Irradiance</subject><subject>Learning</subject><subject>Load modeling</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Optimal control</subject><subject>Prediction models</subject><subject>Predictive models</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEURYMoWKt7wU3AderL98yytlULhQqt6yEzk5SUzqQm00L_vVMrrt7i3nMfHIQeKYwohfxlPZ-NGDAYsVxnKs-v0IBKqUmei-waDYDpjAAIdYvuUtoCUCGpHKC4sjtHFtbE1rcbvNx3vjE7PAltF8MOuxDxvLJk1YVoNhaPfTxnte98-AVWp9TZJuGjN3hqOkNeTbI1HtemXzpaPD21pvEV_oxhE03T9Mw9unFml-zD3x2ir7fZevJBFsv3-WS8IBXnWUdUVmeZAsddLUpOqXOqdlJlymnKS8mUcE4DaGZKSZWVjJXcCekoszk3PTdEz5fdfQzfB5u6YhsOse1fFkxoUJwzLfoWXFpVDClF64p97BXEU0GhOJsterPF2WzxZ7ZHni6It9b-13PgjAvOfwDXFnSE</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Wei, Qinglai</creator><creator>Liao, Zehua</creator><creator>Song, Ruizhuo</creator><creator>Zhang, Pinjia</creator><creator>Wang, Zhuo</creator><creator>Xiao, Jun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-6693-2738</orcidid><orcidid>https://orcid.org/0000-0002-2735-6969</orcidid><orcidid>https://orcid.org/0000-0001-7002-9800</orcidid><orcidid>https://orcid.org/0000-0002-1288-956X</orcidid><orcidid>https://orcid.org/0000-0002-1799-3948</orcidid></search><sort><creationdate>20210401</creationdate><title>Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming</title><author>Wei, Qinglai ; Liao, Zehua ; Song, Ruizhuo ; Zhang, Pinjia ; Wang, Zhuo ; Xiao, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-68d8860f3fd4b311ff6df5686f713b5264ff70072ab516e522b3f45f12e93a0f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive control</topic><topic>Adaptive dynamic programming (ADP)</topic><topic>Air conditioning</topic><topic>Algorithms</topic><topic>Control methods</topic><topic>cooling load prediction</topic><topic>Cooling loads</topic><topic>Dynamic programming</topic><topic>ice-storage air conditioning (IAC)</topic><topic>Irradiance</topic><topic>Learning</topic><topic>Load modeling</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Optimal control</topic><topic>Prediction models</topic><topic>Predictive models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Qinglai</creatorcontrib><creatorcontrib>Liao, Zehua</creatorcontrib><creatorcontrib>Song, Ruizhuo</creatorcontrib><creatorcontrib>Zhang, Pinjia</creatorcontrib><creatorcontrib>Wang, Zhuo</creatorcontrib><creatorcontrib>Xiao, Jun</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wei, Qinglai</au><au>Liao, Zehua</au><au>Song, Ruizhuo</au><au>Zhang, Pinjia</au><au>Wang, Zhuo</au><au>Xiao, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>68</volume><issue>4</issue><spage>3599</spage><epage>3608</epage><pages>3599-3608</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>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. 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| subjects | Adaptive control Adaptive dynamic programming (ADP) Air conditioning Algorithms Control methods cooling load prediction Cooling loads Dynamic programming ice-storage air conditioning (IAC) Irradiance Learning Load modeling neural network Neural networks Optimal control Prediction models Predictive models |
| title | Self-Learning Optimal Control for Ice-Storage Air Conditioning Systems via Data-Based Adaptive Dynamic Programming |
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