Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/145
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dc.contributor.authorKalogirou, Soteris A.en
dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.otherΚαλογήρου, Σωτήρης Α.-
dc.date.accessioned2009-05-27T05:33:31Zen
dc.date.accessioned2013-05-17T05:22:48Z-
dc.date.accessioned2015-12-02T10:07:37Z-
dc.date.available2009-05-27T05:33:31Zen
dc.date.available2013-05-17T05:22:48Z-
dc.date.available2015-12-02T10:07:37Z-
dc.date.issued2005en
dc.identifier.citationInternational Journal of Computer Applications in Technology 2005 - Vol. 22, No.2/3 pp. 90 - 103en
dc.identifier.urihttp://ktisis.cut.ac.cy/handle/10488/145en
dc.description.abstractThe objective of this work is to use artificial intelligence methods for the optimal design of solar energy systems. The lifecycle savings of the system is used as the optimisation parameter. The variable parameters in this optimisation are the collector area, slope and mass flow rate and the volume of the storage tank. An artificial neural network is trained, using the results of a small number of simulations carried out with TRNSYS program, to learn the correlation of the above variable parameters on the auxiliary energy required by the system from which the lifecycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of the variable parameters, which maximises lifecycle savings. As an example, the optimisation of a large hot water system is presented. The optimum solution obtained from the present methodology is achieved very quickly as compared to the time required to obtain the same solution by the traditional trial and error method, which would require thousands of runs of TRNSYS to cover all possible combinations considered by the genetic algorithm.en
dc.formatpdfen
dc.language.isoenen
dc.publisherInderscience Enterprisesen
dc.rightsCopyright © 2004-2006 Inderscience Enterprises Limited. All rights reserved.en
dc.subjectArtificial Neural Networks (ANN)en
dc.subjectGenetic algorithmsen
dc.subjectOptimisationen
dc.subjectSolar systemsen
dc.subjectSolar energyen
dc.subjectSolar poweren
dc.subjectArtificial intelligenceen
dc.subjectOptimal designen
dc.subjectLife cycle savingsen
dc.subjectCollector areaen
dc.titleUse of artificial intelligence for the optimal design of solar systemsen
dc.typeArticleen
dc.journalsSubscription-
dc.identifier.doi10.1504/IJCAT.2005.006940en
dc.dept.handle123456789/54en
item.languageiso639-1other-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-3654-1437-
crisitem.author.parentorgFaculty of Engineering and Technology-
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