Non-iterative neural-like predictor for solar energy in Libya

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dc.contributor.authorTkachenko, R.
dc.contributor.authorKutucu, H.
dc.contributor.authorIzonin, I.
dc.contributor.authorDoroshenko, A.
dc.contributor.authorTsymbal, Y.
dc.date.accessioned2024-09-29T16:22:35Z
dc.date.available2024-09-29T16:22:35Z
dc.date.issued2018
dc.departmentKarabük Üniversitesien_US
dc.descriptionBWT Group; DataArt; Logicify; Oleksandr Spivakovsky's Educational Foundation (OSEF); Taras Shevchenko National University of Kyiven_US
dc.description14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume I: Main Conference, ICTERI 2018 -- 14 May 2018 through 17 May 2018 -- Kyiv -- 136760en_US
dc.description.abstractIn this paper, a new method for predicting the solar radiation potential in Libya was developed. It is constructed on the basis of the combined use of RBF and non-iterative paradigm of the artificial neural networks construction - the Successive Geometric Transformations Model. This method has the advantages of both approaches - the high prediction accuracy from RBF characteristics and fast non-iterative learning provided by the Successive Geometric Transformations Model. A series of practical experiments were conducted. The training model contained 1440 vectors of the monthly solar radiation, which recorded in 25 Libya's cities from 2010 to 2015. The test model contained 360 data's vectors. Comparison of the proposed method with existing ones is presented. The proposed method showed the best prediction results (MAPE, RMSE) compared to SVM, Linear Regression, the linear Neural-like structure of the Successive Geometric Transformation Model (SGTM), and the RBF based on the NLS SGTM. The proposed approach can be used in different areas, such as e-commerce, material science, images processing and others, especially in Big Data cases. © 2018 CEUR-WS. All rights reserved.en_US
dc.identifier.endpage45en_US
dc.identifier.issn1613-0073
dc.identifier.scopus2-s2.0-85048364445en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage35en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/10165
dc.identifier.volume2105en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCEUR-WSen_US
dc.relation.ispartofCEUR Workshop Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeural-Like Structureen_US
dc.subjectRenewable Energyen_US
dc.subjectSolar-Radiation Potentialen_US
dc.subjectSuccessive Geometric Transformations Modelen_US
dc.titleNon-iterative neural-like predictor for solar energy in Libyaen_US
dc.typeConference Objecten_US

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