Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine

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dc.authorid0000-0003-0710-0867en_US
dc.authorid0000-0002-4893-6014en_US
dc.authorid0000-0002-0698-3525en_US
dc.authorid0000-0001-7789-6376en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.contributor.authorSezgin, Necmettin
dc.contributor.authorÖztekin, Abdulkerim
dc.contributor.authorTağluk, Mehmet Emin
dc.date.accessioned2019-07-04T13:10:29Z
dc.date.available2019-07-04T13:10:29Z
dc.date.issued2017-11-02en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractEstimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using selected features the required memory, equipment and communication costs are also decreased in real time applications. Feature selection by extreme learning machine method was used in determining relevant features. The short-term power loads of two houses (one of them has a power generation capability) were used in tests and achieved results showed lower error rates were obtained by using less number of features.en_US
dc.identifier.citationErtuğrul, Ö F., Sezgin, N., Öztekin, A., Tağluk, M. E. (2017). Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 16-17 Sept. 2017, Malatya, Turkey. https://doi.org/10.1109/idap.2017.8090345en_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.isbn978-1-5386-1881-3
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/idap.2017.8090345
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2185
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/idap.2017.8090345en_US
dc.relation.journal2017 International Artificial Intelligence and Data Processing Symposium (IDAP)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectExtreme Learning Machineen_US
dc.subjectFeature Selectionen_US
dc.subjectShort-Term Power Loaden_US
dc.titleDetermining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machineen_US
dc.typeConference Objecten_US

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Mühendislik-Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü, Bildiri Metinleri Koleksiyonu
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