A Comparative Study of Artificial Neural Network and ANFIS for Short Term Load Forecasting

dc.contributor.authorCevik, Hasan Huseyin
dc.contributor.authorCunkas, Mehmet
dc.date.accessioned2020-03-26T18:49:06Z
dc.date.available2020-03-26T18:49:06Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) -- OCT 23-25, 2014 -- Pitesti, ROMANIAen_US
dc.description.abstractShort term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season and temperature data of Turkey between years of 2009-2011, the prediction is carried out for 2012. The mean absolute percentage errors for ANFIS and ANN models are found as 1.85 and 2.02, respectively in all days except holidays of 2012.en_US
dc.description.sponsorshipIEEE, IEEE Romania sect, IEEE Ind Appl Socen_US
dc.identifier.isbn978-1-4799-5479-7
dc.identifier.issn2378-7147en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30528
dc.identifier.wosWOS:000380489500041en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofPROCEEDINGS OF THE 2014 6TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE (ECAI)en_US
dc.relation.ispartofseriesInternational Conference on Electronics Computers and Artificial Intelligence
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectshort term load forecastingen_US
dc.subjectartificial neural networksen_US
dc.subjectANFISen_US
dc.titleA Comparative Study of Artificial Neural Network and ANFIS for Short Term Load Forecastingen_US
dc.typeConference Objecten_US

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