Forecasting of natural gas consumption with neural network and neuro fuzzy system

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dc.authoridkaynar, oguz -- 0000-0003-2387-4053;en_US
dc.contributor.authorKaynar, Oguz
dc.contributor.authorYilmaz, Isik
dc.contributor.authorDemirkoparan, Ferhan
dc.date.accessioned2019-07-27T12:10:23Z
dc.date.accessioned2019-07-28T10:06:29Z
dc.date.available2019-07-27T12:10:23Z
dc.date.available2019-07-28T10:06:29Z
dc.date.issued2011
dc.department[Kaynar, Oguz -- Demirkoparan, Ferhan] Cumhuriyet Univ, Dept Management Informat Syst, Sivas, Turkey -- [Yilmaz, Isik] Cumhuriyet Univ, Dept Geosci, Sivas, Turkeyen_US
dc.description.abstractThe prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared.en_US
dc.identifier.endpage238en_US
dc.identifier.issn1308-772X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-77957983577en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage221en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12418/9677
dc.identifier.volume26en_US
dc.identifier.wosWOS:000278087700011en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSILA SCIENCEen_US
dc.relation.ispartofENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCHen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANNen_US
dc.subjectANFISen_US
dc.subjectARIMAen_US
dc.subjectNatural gasen_US
dc.subjectForecastingen_US
dc.titleForecasting of natural gas consumption with neural network and neuro fuzzy systemen_US
dc.typeArticleen_US

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