Electricity Load Forecasting via ANN Approach in Turkish Electricity Markets

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dc.contributor.authorGözksöz, Fazıl
dc.contributor.authorFiliz, Fahrettin
dc.contributor.departmentİşletmetr_TR
dc.contributor.facultySiyasal Bilgiler Fakültesitr_TR
dc.date.accessioned2021-10-22T07:04:47Z
dc.date.available2021-10-22T07:04:47Z
dc.date.issued2020-12-31
dc.description.abstractForecasting electricity load has become the essential task for electric utilities, power plants and regulators. It is essential that electricity load forecasts, which are a vital necessity of energy policies, produce healthy and reliable results. Artificial neural networks (ANN) can learn complex and nonlinear relationships. This article introduces 400 different ANN models for electricity load forecasting. Model performances have compared with Mean Absolute Percentage Error (MAPE) and Diebold-Mariano (DM) test. The electricity load data used for this study range from 2014 to 2016. The variation in forecasting ability of ANN for different models has also discussed. Levenberg-Marquardt (LM) with log-sigmoid transfer function trains the best performance ANN model.tr_TR
dc.description.indexTrdizintr_TR
dc.identifier.endpage184tr_TR
dc.identifier.issn/e-issn2636-8544
dc.identifier.issue2tr_TR
dc.identifier.startpage170tr_TR
dc.identifier.urihttps://doi.org/10.33721/by.834285tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/75653
dc.identifier.volume3tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesitr_TR
dc.relation.isversionof10.33721/by.834285tr_TR
dc.relation.journalBilgi Yönetimitr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.subjectElectricity Load Forecasttr_TR
dc.subjectArtificial Neural Networktr_TR
dc.subjectTurkeytr_TR
dc.titleElectricity Load Forecasting via ANN Approach in Turkish Electricity Marketstr_TR
dc.title.alternativeTürk Elektrik Piyasalarında YSA Yaklaşımıyla Elektrik Yükü Tahminitr_TR
dc.typeArticletr_TR

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Cilt:03 Sayı:02 (2020)