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Sensitivity of artificial neural network based model for photovoltaic system actual performance

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dc.citedby4
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.accessioned2023-05-16T02:45:59Z
dc.date.available2023-05-16T02:45:59Z
dc.date.issued2014
dc.description.abstractA novel prediction model for the output current of PV module is proposed in this paper. The proposed model is based on cascade-forward back propagation artificial neural network with two inputs and one output. Solar radiation and ambient temperature are the inputs and the predicted current is the output. Experiment data for a 1.4 kWp PV systems installed in Sohar city, Oman are utilized in developing the proposed model. These data has an interval of 2 seconds in order to consider the uncertainty of the system's output current. In order to evaluate the accuracy of the neural network, three statistical values are used namely mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. The results show that the MAPE, MBE and RMSE of the proposed model are 7.08%, -4.98% and 7.8%, respectively © 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7062449
dc.identifier.doi10.1109/PECON.2014.7062449
dc.identifier.epage244
dc.identifier.scopus2-s2.0-84946691724
dc.identifier.spage241
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84946691724&doi=10.1109%2fPECON.2014.7062449&partnerID=40&md5=06afc94a56e8cf06df1642e4165f0028
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21908
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleConference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014
dc.titleSensitivity of artificial neural network based model for photovoltaic system actual performanceen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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