Publication:
Modeling of photovoltaic array output current based on actual performance using artificial neural networks

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dc.citedby6
dc.contributor.authorAmeen A.M.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorKhatib T.en_US
dc.contributor.authorid56602552200en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid31767521400en_US
dc.date.accessioned2023-05-29T05:59:58Z
dc.date.available2023-05-29T05:59:58Z
dc.date.issued2015
dc.descriptionErrors; Neural networks; Photovoltaic cells; Regression analysis; Generalized regression; Generalized regression neural networks; Mean absolute percentage error; Mean bias errors; Photovoltaic arrays; Photovoltaic modules; Prediction accuracy; Root mean square errors; Mean square erroren_US
dc.description.abstractThis paper presents prediction models for photovoltaic (PV) module's output current. The proposed models are based on empirical, statistical, and artificial neural networks. The adopted artificial neural networks are generalized regression, feed forward, and cascaded forward neural networks. The proposed models have two inputs, namely, solar radiation and ambient temperature, while system's output current is the output. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. These data are recorded every 10 seconds in order to consider the uncertainty of system's output current. Three statistical values are used to evaluate the accuracy of the proposed models, namely, mean absolute percentage error, mean bias error, and root mean square error. A comparison between the proposed models in terms of prediction accuracy is conducted. The results show that the generalized regression neural network based model exceeds the other models. The mean absolute percentage error, root mean square error, and mean bias error of the generalized regression neural network model are 4.97%, 5.67%, and -1.17%, respectively. � 2015 AIP Publishing LLC.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo53107
dc.identifier.doi10.1063/1.4931464
dc.identifier.issue5
dc.identifier.scopus2-s2.0-84942770133
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84942770133&doi=10.1063%2f1.4931464&partnerID=40&md5=4b8d2db05622f6ae008b12d05556b775
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22275
dc.identifier.volume7
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleJournal of Renewable and Sustainable Energy
dc.titleModeling of photovoltaic array output current based on actual performance using artificial neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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