Solar panel modelling through computational intelligence techniques

Basit Öğe Kaydı
dc.authorid0000-0002-4094-1273en_US
dc.authorid0000-0002-8777-5444en_US
dc.authorid0000-0003-2649-3061en_US
dc.authorid0000-0002-4982-6212en_US
dc.authorid0000-0003-2649-3061en_US
dc.contributor.authorFerrari, Stefano
dc.contributor.authorLazzaroni, Massimo
dc.contributor.authorPiuri, Vincenzo
dc.contributor.authorSalman, Ayse
dc.contributor.authorCristaldi, Loredana
dc.contributor.authorFaifer, Marco
dc.contributor.authorToscani, Sergio
dc.date.accessioned2024-07-12T21:51:53Z
dc.date.available2024-07-12T21:51:53Z
dc.date.issued2016en_US
dc.departmentMaltepe Üniversitesien_US
dc.description.abstractThe efficiency of a solar panel depends on several factors. In particular, the ability to operate in the Maximum Power Point (MPP) condition is required in order to optimize the energy production. The ability to identify and reach the MPP condition is therefore critical to an efficient conversion of the photovoltaic energy. Several techniques to tackle this problem are reported in literature. They differ for the input variables used to compute the MPP as well as the structure of the controller that makes use of the prediction. We focus only on the prediction of the MPP which is related only to the former aspect. In this paper, several computational intelligence paradigms (namely, Fuzzy C-Means, Radial Basis Function Networks, k-Nearest Neighbor, and Feed-forward Neural Networks) are challenged in the task of identifying the MPP power from the working condition directly measurable from the solar panel, such as the voltage, V, the current, I, and the temperature, T, of the panel. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.measurement.2016.07.032
dc.identifier.endpage580en_US
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-84979950199en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage572en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.measurement.2016.07.032
dc.identifier.urihttps://hdl.handle.net/20.500.12415/8328
dc.identifier.volume93en_US
dc.identifier.wosWOS:000386869600066en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTDen_US
dc.relation.ispartofMEASUREMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY02874
dc.subjectSolar panel modellingen_US
dc.subjectNeural networksen_US
dc.subjectRadial basis function networksen_US
dc.subjectMeasurementen_US
dc.titleSolar panel modelling through computational intelligence techniquesen_US
dc.typeArticle
dspace.entity.typePublication

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu