Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region

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dc.authoridCELIK, Ozgur/0000-0002-7683-2415
dc.contributor.authorBozkurt, Helin
dc.contributor.authorMacit, Ramazan
dc.contributor.authorCelik, Ozgur
dc.contributor.authorTeke, Ahmet
dc.date.accessioned2025-01-06T17:36:28Z
dc.date.available2025-01-06T17:36:28Z
dc.date.issued2022
dc.description.abstractSolar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data. Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant. The obtained results are compared with the output power generation data of a 350 kW solar power plant. The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE). FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.
dc.identifier.doi10.55730/1300-0632.3921
dc.identifier.endpage2030
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85142239744
dc.identifier.scopusqualityQ2
dc.identifier.startpage2013
dc.identifier.trdizinid1142464
dc.identifier.urihttps://doi.org/10.55730/1300-0632.3921
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1142464
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1895
dc.identifier.volume30
dc.identifier.wosWOS:000884407400001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectArtificial neural networks
dc.subjectlong short-term memory
dc.subjectmultilayer perceptron
dc.subjectphotovoltaic power forecasting
dc.subjectglobal solar irradiation forecasting
dc.titleEvaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region
dc.typeArticle

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