Long-term Electrical Load Forecasting based on Economic and Demographic Data for Turkey

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dc.contributor.authorCetinkaya, Nurettin
dc.date.accessioned2020-03-26T18:42:23Z
dc.date.available2020-03-26T18:42:23Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description14th IEEE International Symposium on Computational Intelligence and Informatics (CINTI) -- NOV 19-21, 2013 -- Budapest, HUNGARYen_US
dc.description.abstractLoad forecasting is very important to operate the electric power systems. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Long term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption data, national incoming, urbanization rate, population increasing rate and as well as other economic parameters. Artificial Neural Network (ANN) and Artificial Neural Fuzzy Inference System (ANFIS) are the famous artificial intelligence methods and have widely used to solve forecasting problems in literature. In this study, artificial intelligence methods and mathematical modeling (MM) are used to forecast long term energy consumption and peak load for Turkey. The four different input data are used to obtain two different outputs in all three methods. Using the four different variables especially in mathematical modeling has been a novelty for Turkey case study. The results obtained from ANFIS, ANN and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error (MAE) are used.en_US
dc.description.sponsorshipIEEEen_US
dc.identifier.endpage223en_US
dc.identifier.isbn978-1-4799-0194-4; 978-1-4799-0197-5
dc.identifier.issn2380-8586en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage219en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29620
dc.identifier.wosWOS:000345626300037en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof14TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI)en_US
dc.relation.ispartofseriesInternational Symposium on Computational Intelligence and Informatics
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleLong-term Electrical Load Forecasting based on Economic and Demographic Data for Turkeyen_US
dc.typeConference Objecten_US

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