The optimized artificial neural network model with Levenberg-Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey

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dc.authoridCELIK, Ozgur/0000-0002-7683-2415
dc.authoridYildirim, H. Basak/0000-0003-0355-7448
dc.authoridteke, ahmet/0000-0003-2610-9576
dc.contributor.authorCelik, Ozgur
dc.contributor.authorTeke, Ahmet
dc.contributor.authorYildirim, H. Basak
dc.date.accessioned2025-01-06T17:38:10Z
dc.date.available2025-01-06T17:38:10Z
dc.date.issued2016
dc.description.abstractAn accurate knowledge on global solar radiation is particularly required for proper placement and design of solar energy conversion systems. While the meteorological data are measured at most of the weather stations, global solar radiation measurement is not always performed due to high cost of the measurement devices and their operation and maintenance requirements. Therefore, several linear, non-linear and soft computing models are developed to estimate the solar radiation owing to being more economical when compared to installing pyranometers and these models provide satisfactory results. However, it is crucial to choose the most appropriate model for a specific purpose and region. The primary objective of this study is to optimize the performance of the artificial neural network model in order to realize an efficient estimation of solar radiation for Eastern Mediterranean Region of Turkey. Estimation performances are discussed for different structures of neural network by taking into account the number and quality of input features, learning algorithms, number of hidden neurons, correlation between network outputs and targets, and statistical error analysis methods. The presented model indicates that the artificial neural network models illustrate promising in the estimation of monthly mean daily global solar radiation by using commonly available data. In order to indicate the superiority of the performance of the model, it is evaluated with various test years, which are not used for training stage of the model. The presented model provides superior relationship between the estimated and measured values. The test results showed that the coefficient of determination and mean absolute percentage error between the optimized artificial neural network estimations and measured values for testing datasets are higher than 99% and %5, respectively. 2015 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipElectrical, Electronics and Informatics Research Group of Scientific and Technological Research Council of Turkey (TUBITAK) [EEEAG-114Y391]
dc.description.sponsorshipThe authors gratefully acknowledge the Electrical, Electronics and Informatics Research Group of Scientific and Technological Research Council of Turkey (TUBITAK) for the research project (Project Number: EEEAG-114Y391).
dc.identifier.doi10.1016/j.jclepro.2015.12.082
dc.identifier.endpage12
dc.identifier.issn0959-6526
dc.identifier.issn1879-1786
dc.identifier.scopus2-s2.0-84960080573
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1016/j.jclepro.2015.12.082
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2508
dc.identifier.volume116
dc.identifier.wosWOS:000370885800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofJournal of Cleaner Production
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectGlobal solar radiation estimation
dc.subjectArtificial neural network
dc.subjectEastern Mediterranean Region
dc.titleThe optimized artificial neural network model with Levenberg-Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey
dc.typeArticle

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