An application of artificial neural networks to assessment of the wind energy potential in Libya

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dc.contributor.authorKutucu, H.
dc.contributor.authorAlmryad, A.
dc.date.accessioned2024-09-29T16:20:44Z
dc.date.available2024-09-29T16:20:44Z
dc.date.issued2017
dc.departmentKarabük Üniversitesien_US
dc.description7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2016 -- 18 December 2016 through 20 December 2016 -- Hammamet -- 128221en_US
dc.description.abstractWe modeled in this paper the variation of wind speed as a renewable energy in Mediterranean Sea of Libya (North of Africa) using an artificial neural network (ANN). We developed multi-layer, feed-forward, back-propagation artificial neural networks for prediction monthly mean wind speed. The monthly mean wind speed data of 25 cities in Libya were monitored during the period of six years from 2010 to 2015. Meteorological (mean temperature, relative humidity and mean sunshine duration) and geographical data (latitude, longitude and altitude) are used as the inputs and the wind speed is used as the output of the ANN. The experimental results show that the correlation coefficients between the predicted and measured wind speeds for training data sets are higher than 0.99. Therefore, the ANN model can be used with high prediction accuracy at locations where wind speed data are not measured. © 2016 IEEE.en_US
dc.identifier.doi10.1109/SETIT.2016.7939904
dc.identifier.endpage409en_US
dc.identifier.isbn978-150904712-3
dc.identifier.scopus2-s2.0-85021456792en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage405en_US
dc.identifier.urihttps://doi.org/10.1109/SETIT.2016.7939904
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9286
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2016en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networken_US
dc.subjectLibyaen_US
dc.subjectpredictionen_US
dc.subjectrenewable energyen_US
dc.subjectwind speeden_US
dc.titleAn application of artificial neural networks to assessment of the wind energy potential in Libyaen_US
dc.typeConference Objecten_US

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