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Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey

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dc.contributor.author Altan Dombaycı, Ömer
dc.contributor.author Gölcü, Mustafa
dc.date.accessioned 2019-08-16T12:11:40Z
dc.date.available 2019-08-16T12:11:40Z
dc.date.issued 2009
dc.identifier.issn 0960-1481
dc.identifier.uri https://hdl.handle.net/11499/6837
dc.identifier.uri https://doi.org/10.1016/j.renene.2008.07.007
dc.description.abstract The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict daily mean ambient temperatures in Denizli, south-western Turkey. In order to train the model, temperature values, measured by The Turkish State Meteorological Service over three years (2003-2005) were used as training data and the values of 2006 were used as testing data. In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. The predictions were performed by taking different number of hidden layer neurons between 3 and 30. The best result was obtained when the number of the neurons is 6. The selected ANN model of a multi-layer consists of 3 inputs, 6 hidden neurons and 1 output. Training of the network was performed by using Levenberg-Marquardt (LM) feed-forward backpropagation algorithms. A computer program was performed under Matlab 6.5 software. For each network, fraction of variance (R2) and root-mean squared error (RMSE) values were calculated and compared. The results show that the ANN approach is a reliable model for ambient temperature prediction. © 2008 Elsevier Ltd. All rights reserved. en_US
dc.language.iso en en_US
dc.relation.ispartof Renewable Energy en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Ambient temperature en_US
dc.subject Artificial neural network en_US
dc.subject Prediction en_US
dc.subject Backpropagation en_US
dc.subject Backpropagation algorithms en_US
dc.subject Forecasting en_US
dc.subject Image classification en_US
dc.subject MATLAB en_US
dc.subject Military operations en_US
dc.subject Neural networks en_US
dc.subject Neurons en_US
dc.subject Temperature en_US
dc.subject Ambient temperatures en_US
dc.subject Ann models en_US
dc.subject Artificial neural network models en_US
dc.subject Artificial neural networks en_US
dc.subject Case studies en_US
dc.subject Computer programs en_US
dc.subject Hidden layer neurons en_US
dc.subject Hidden layers en_US
dc.subject Hidden neurons en_US
dc.subject Levenberg-marquardt en_US
dc.subject Optimal network architectures en_US
dc.subject Reliable models en_US
dc.subject Squared errors en_US
dc.subject Temperature values en_US
dc.subject Testing datums en_US
dc.subject Training algorithms en_US
dc.subject Training datums en_US
dc.subject Turkishs en_US
dc.subject Network architecture en_US
dc.subject air temperature en_US
dc.subject algorithm en_US
dc.subject artificial neural network en_US
dc.subject software en_US
dc.subject Denizli [Turkey] en_US
dc.subject Eurasia en_US
dc.subject Turkey en_US
dc.title Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey en_US
dc.type Article en_US
dc.identifier.volume 34 en_US
dc.identifier.issue 4 en_US
dc.identifier.startpage 1158
dc.identifier.startpage 1158 en_US
dc.identifier.endpage 1161 en_US
dc.identifier.doi 10.1016/j.renene.2008.07.007
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.identifier.scopus 2-s2.0-56049113721 en_US
dc.identifier.wos WOS:000262203000030 en_US

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