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dc.contributor.authorAkyol, Uğur
dc.contributor.authorAkal, Dinç
dc.contributor.authorDurak, Ahmet
dc.date.accessioned2022-05-11T14:02:57Z
dc.date.available2022-05-11T14:02:57Z
dc.date.issued2021
dc.identifier.issn1556-7036
dc.identifier.urihttps://doi.org/10.1080/15567036.2021.1928337
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4549
dc.description.abstractThis study deals with the estimation of power output and also thermodynamic analysis of two different photovoltaic panels. One of the panels is a standard photovoltaic module without fins (SPV), and the other one is a photovoltaic module with fins (FPV). First, a multi-layer feed-forward neural network structure is designed to estimate the daily power produced by photovoltaic modules. Furthermore, energy and exergy analyses were carried out to compare the performance of SPV and FPV panels. According to the thermodynamic analysis results using the experimental data obtained for two days (July 3, 2020 and August 4, 2020), it was calculated that the energy efficiency increased by a maximum of 8.77% and the exergy efficiency increased by a maximum of 25.9% in the FPV panel compared to the SPV panel. Moreover, considering the data obtained for each day during three months (July, August, and September), the total energy production increase in the FPV panel is approximately 6.7% compared to the SPV panel. © 2021 Taylor & Francis Group, LLC.en_US
dc.language.isoengen_US
dc.publisherBellwether Publishing, Ltd.en_US
dc.identifier.doi10.1080/15567036.2021.1928337
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectartificial neural networken_US
dc.subjectenergy analysisen_US
dc.subjectexergy analysisen_US
dc.subjectfinen_US
dc.subjectpassive coolingen_US
dc.subjectPhotovoltaicen_US
dc.subjectpredictionen_US
dc.subjectEnergy efficiencyen_US
dc.subjectExergyen_US
dc.subjectFeedforward neural networksen_US
dc.subjectFins (heat exchange)en_US
dc.subjectPhotovoltaic cellsen_US
dc.subjectThermoanalysisen_US
dc.subjectEnergy and exergy analysisen_US
dc.subjectExergy efficienciesen_US
dc.subjectMultilayer feedforward neural networksen_US
dc.subjectPhotovoltaic modulesen_US
dc.subjectPhotovoltaic panelsen_US
dc.subjectPower out puten_US
dc.subjectThermo dynamic analysisen_US
dc.subjectTotal energyen_US
dc.subjectMultilayer neural networksen_US
dc.titleEstimation of power output and thermodynamic analysis of standard and finned photovoltaic panelsen_US
dc.typearticleen_US
dc.relation.ispartofEnergy Sources, Part A: Recovery, Utilization and Environmental Effectsen_US
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.departmentMeslek Yüksekokulları, Hayrabolu Meslek Yüksekokulu, Motorlu Araçlar ve Ulaştırma Teknolojileri Bölümüen_US
dc.institutionauthorAkyol, Uğur
dc.institutionauthorDurak, Ahmet
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid6506635786
dc.authorscopusid23984042700
dc.authorscopusid56575516000
dc.identifier.wosWOS:000656327700001en_US
dc.identifier.scopus2-s2.0-85107371393en_US


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