Modeling of Solar Energy Potential in Libya using an Artificial Neural Network Model

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dc.authoridKutucu, Hakan/0000-0001-7144-7246
dc.contributor.authorKutucu, Hakan
dc.contributor.authorAlmryad, Ayad
dc.date.accessioned2024-09-29T16:11:23Z
dc.date.available2024-09-29T16:11:23Z
dc.date.issued2016
dc.departmentKarabük Üniversitesien_US
dc.description1st IEEE International Conference on Data Stream Mining and Processing (DSMP) -- AUG 23-27, 2016 -- Lviv, UKRAINEen_US
dc.description.abstractIn this work, we develop an artificial neural network model to predict the potential of solar power in Libya. We use multilayered, feed-forward, back-propagation neural networks for the mean monthly solar radiation using the data of 25 cities spread over Libya for the period of 6 years (2010-2015). Meteorological and geographical data (longitude, latitude, and altitude, month, mean sunshine duration, mean temperature, and relative humidity) are used as input to the network. The solar radiation is in the output layer of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation for training and testing datasets are higher than 98%. Hence, the predictions from ANN model in locations where solar radiation data are not available has a high reliability.en_US
dc.description.sponsorshipIEEE,softserve,GlobalLogic,CIKLUM,Lviv IT Cluster,Lviv City Council,Inst Territories Transformat,THEY,ARENA,NEADEVIS,ykpTeaekom,IEEE Ukraine Sect,IEEE Ukraine Sect SP AP C EMC COM Soc Joint Chapter,IEEE Ukraine Sect IM CIS Soc Joint Chapter,IEEE Ukraine Sect AP ED MTT CPMT SSC Soc Joint Chapteren_US
dc.identifier.endpage359en_US
dc.identifier.isbn978-1-5090-3736-0
dc.identifier.startpage356en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8399
dc.identifier.wosWOS:000390239100060en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartofProceedings of the 2016 Ieee First International Conference On Data Stream Mining & Processing (Dsmp)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectsolar-radiation potentialen_US
dc.subjectrenewable energyen_US
dc.subjectLibyaen_US
dc.titleModeling of Solar Energy Potential in Libya using an Artificial Neural Network Modelen_US
dc.typeConference Objecten_US

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