Forecasting net energy consumption using artificial neural network

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dc.contributor.authorSözen, Adnan
dc.contributor.authorAkcayol, M. Ali
dc.contributor.authorArcaklıoğlu, Erol
dc.date.accessioned2020-06-25T17:41:11Z
dc.date.available2020-06-25T17:41:11Z
dc.date.issued2006
dc.departmentKırıkkale Üniversitesi
dc.descriptionARCAKLIOGLU, Erol/0000-0001-8073-5207;
dc.description.abstractThe main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using the artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Logistic sigmoid transfer function was used in the network. In order to train the neural network, population, and gross generation, installed capacity and years is used in input layer of network. The net energy consumption is in output layer. The input values in 1965, 1981, and 1997 are only used as test data to confirm this method. The statistical coefficient of multiple determinations (R-2-value) is equal to 0.9999 and 1 for training and test data, respectively. According to the results, the NEC using the ANN technique has been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.en_US
dc.identifier.citationclosedAccessen_US
dc.identifier.doi10.1080/009083190881562
dc.identifier.endpage155en_US
dc.identifier.issn1556-7249
dc.identifier.issn1556-7257
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-33748492958
dc.identifier.scopusqualityQ1
dc.identifier.startpage147en_US
dc.identifier.urihttps://doi.org/10.1080/009083190881562
dc.identifier.urihttps://hdl.handle.net/20.500.12587/3649
dc.identifier.volume1en_US
dc.identifier.wosWOS:000241491900004
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofEnergy Sources Part B-Economics Planning And Policy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectenergyen_US
dc.subjectconsumptionen_US
dc.subjectgross generationen_US
dc.subjectestimationen_US
dc.subjectartificial neural networken_US
dc.subjectTurkeyen_US
dc.titleForecasting net energy consumption using artificial neural networken_US
dc.typeArticle

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