Comparison of feedforward perceptron network with LSTM for solar cell radiation prediction

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dc.authoridTaher, Dr. Fatma/0000-0001-8358-9081
dc.contributor.authorÖzdemir, Tuğba
dc.contributor.authorTaher, Fatma
dc.contributor.authorAyinde, Babajide O.
dc.contributor.authorZurada, Jacek M.
dc.contributor.authorTüzün Özmen, Özge
dc.date.accessioned2023-03-22T19:47:23Z
dc.date.available2023-03-22T19:47:23Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractIntermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R-2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R-2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) - Scientific and Technological Research Council of Turkey (TUBITAK) [1059B141800505]en_US
dc.description.sponsorshipThis study was supported by 1059B141800505 from The Scientific and Technological Research Council of Turkey (TUBITAK). This research was funded by The Scientific and Technological Research Council of Turkey (TUBITAK), grant number 1059B141800505.en_US
dc.identifier.doi10.3390/app12094463
dc.identifier.issn2076-3417
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-85129861832en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/app12094463
dc.identifier.urihttps://hdl.handle.net/20.500.14034/667
dc.identifier.volume12en_US
dc.identifier.wosWOS:000795317400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.journalApplied Sciences-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectrenewable energyen_US
dc.subjectsolar energyen_US
dc.subjectartificial neural networken_US
dc.subjectdeep learningen_US
dc.subjectLSTMen_US
dc.subjectradiation predictionen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectEnergy-Consumptionen_US
dc.subjectOutput Poweren_US
dc.subjectEfficiencyen_US
dc.subjectPerformanceen_US
dc.subjectSystemsen_US
dc.subjectModelsen_US
dc.subjectEmissionsen_US
dc.titleComparison of feedforward perceptron network with LSTM for solar cell radiation predictionen_US
dc.typeArticleen_US

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