PREDICTION OF HOURLY SOLAR RADIATION USING AN ARTIFICIAL NEURAL NETWORK

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dc.contributor.authorSolmaz, Ozgur
dc.contributor.authorOzgoren, Muammer
dc.date.accessioned2020-03-26T18:15:43Z
dc.date.available2020-03-26T18:15:43Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description17th International Conference on Soft Computing MENDEL 2011 -- JUN 15-17, 2011 -- Brno, CZECH REPUBLICen_US
dc.description.abstractThe aim of the presented study is to apply artificial neural networks (ANNs) method for prediction hourly solar radiation of the selected six provinces of Turkey. Six neurons which receive input signals of latitude, longitude, altitude, day of the year, hour of the day and hourly mean atmospheric air temperature were used in the input layer of the network. One neuron producing corresponding output signal of the hourly solar radiation was utilized in the output layer of the network. The model for training and testing in the formed ANNs was analyzed. Neuron numbers in the hidden layer (from 6 to 30 neurons step by step) and epoch numbers for 100 epochs were tested for different values. The obtained results for this model was compared by using different neurons, mean squared error (MSE), coefficient of determination (R-2), mean absolute error (MAE). The best results for the training were obtained as 25 neurons in terms of minimum MSE value of 0.000607. The R-2 values of the ANN for training and testing data of the 25 neurons are determined as 0.9879 and 0.9891 while the MAE values of which are 18.33 W/m(2) and 18.94 W/m(2), respectively.en_US
dc.description.sponsorshipB & R Automat CZ Ltd, Humusoft Ltd, AutoCont CZ Ltden_US
dc.identifier.endpage225en_US
dc.identifier.isbn978-80-214-4302-0
dc.identifier.issn1803-3814en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage218en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26739
dc.identifier.wosWOS:000302647900035en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherBRNO UNIV TECHNOLOGY VUT PRESSen_US
dc.relation.ispartofMENDEL 2011 - 17TH INTERNATIONAL CONFERENCE ON SOFT COMPUTINGen_US
dc.relation.ispartofseriesMendel
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjecthourly solar radiationen_US
dc.subjectsolar energyen_US
dc.subjectpredictionen_US
dc.titlePREDICTION OF HOURLY SOLAR RADIATION USING AN ARTIFICIAL NEURAL NETWORKen_US
dc.typeConference Objecten_US

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