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.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authoridÖztekin, Abdulkerim/0000-0002-0698-3525
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.authorwosidÖztekin, Abdulkerim/ABA-6749-2020
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorSezgin, Necmettin
dc.contributor.authorOztekin, Abdulkerim
dc.contributor.authorTagluk, Mehmet Emin
dc.date.accessioned2024-08-04T20:44:11Z
dc.date.available2024-08-04T20:44:11Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_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.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039913283en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98086
dc.identifier.wosWOS:000426868700185en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 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.subjectShort-term power loaden_US
dc.subjectfeature selectionen_US
dc.subjectextreme learning machineen_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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