Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

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dc.authoridTUMEN OZDIL, N. Filiz/0000-0003-0083-7524
dc.authoridKoroglu, Tahsin/0000-0002-6587-3529
dc.authoridOztekin, Onur Miran/0000-0001-9840-3884
dc.contributor.authorGoncu, Onur
dc.contributor.authorKoroglu, Tahsin
dc.contributor.authorOzdil, Naime Filiz
dc.date.accessioned2025-01-06T17:38:11Z
dc.date.available2025-01-06T17:38:11Z
dc.date.issued2021
dc.description.abstractSince global solar radiation (GSR) is an important parameter for the design, installation, and operation of solar energy-based systems, it is important to have precise information about it. As the indicating devices are expensive and their requirements such as operation and maintenance should be carried out, the measurement of solar radiation cannot be frequently taken. On the other hand, the measurements of different meteorological parameters such as relative humidity and ground surface temperature are more prevalent in meteorology stations. Therefore, the estimation of solar radiation is a significant parameter for the areas where the measurements could not be performed and to complete the missing information in databases. Many different models, software, and simulation programs are utilized to calculate solar radiation data, provide an economic advantage, and obtain high accuracy. The main purpose of this study is to perform an estimation of solar radiation in Adana, where is on the east of the Mediterranean in Turkey, by using an artificial neural network (ANN) model. The best estimation performance is obtained by optimizing the neuron numbers used in the network's hidden layer with the trial and error method. With this aim, hourly data including wind speed, wind direction, humidity, actual pressure, and average temperature are taken as inputs while solar radiation is taken as a target. All these data, which is for 2018, has taken from the Turkish State Meteorological Service. A linear correlation coefficient value has been obtained to be about 0.87313 with the mean square error (MSE) of 5.8262x10(7) W/m(2) for the testing data set. The ANN's testing/validation results show that it has a low MSE, indicating the accuracy and adequacy of the network model. Besides, the predicted ANN output is evaluated to be remarkably close to the measured target data by considering the linear correlation coefficient.
dc.identifier.doi10.18186/thermal.1051313
dc.identifier.endpage2030
dc.identifier.issn2148-7847
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85123590901
dc.identifier.scopusqualityQ3
dc.identifier.startpage2017
dc.identifier.trdizinid1172425
dc.identifier.urihttps://doi.org/10.18186/thermal.1051313
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1172425
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2510
dc.identifier.volume7
dc.identifier.wosWOS:000756697000014
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherYildiz Technical Univ
dc.relation.ispartofJournal of Thermal Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectGlobal solar radiation
dc.subjectArtificial neural network
dc.subjectLevenberg-Marquardt algorithm
dc.subjectMean square error
dc.subjectLinear correlation coefficient
dc.titleEstimation of hourly global solar radiation using artificial neural network in Adana province, Turkey
dc.typeArticle

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