Multi-time series and-time scale modeling for wind speed and wind power forecasting part I: Statistical methods, very short-term and short-term applications

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dc.authorscopusid6602990030
dc.authorscopusid7003371572
dc.authorscopusid35792736300
dc.authorscopusid34976617700
dc.authorscopusid57188997543
dc.contributor.authorColak, Ilhami
dc.contributor.authorSagiroglu, Seref
dc.contributor.authorYesilbudak, Mehmet
dc.contributor.authorKabalci, Ersan
dc.contributor.authorIbrahim Bulbul, H.
dc.date.accessioned2024-09-11T19:59:00Z
dc.date.available2024-09-11T19:59:00Z
dc.date.issued2015
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description4th International Conference on Renewable Energy Research and Applications, ICRERA 2015 -- 22 November 2015 through 25 November 2015 -- Palermo -- 119791en_US
dc.description.abstractThis study concentrates on multi-time series and-time scale modeling in wind speed and wind power forecasting. Different statistical models along with different time horizons are analyzed and evaluated broadly and comprehensively. For this reason, the entire study is divided into two main scientific parts. In case of making a general overview of the entire study, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) methods are employed for multi-time series modeling. Very short-term, short-term, medium-term and long-term scales are utilized for multi-time scale modeling. Specifically, in this part of the entire study, the mentioned statistical models are presented in detail and 10-min and 1-h time series forecasting models are created for the purpose of achieving 10-min and 2-h ahead forecasting, respectively. Many useful outcomes are accomplished for very short-term and short-term wind speed and wind power forecasting. © 2015 IEEE.en_US
dc.identifier.doi10.1109/ICRERA.2015.7418697
dc.identifier.endpage214en_US
dc.identifier.isbn978-147999982-8en_US
dc.identifier.scopus2-s2.0-84964623345en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage209en_US
dc.identifier.urihttps://doi.org/10.1109/ICRERA.2015.7418697
dc.identifier.urihttps://hdl.handle.net/11363/8611
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectforecasting; short-term; Statistical methods; very short-term; wind power; wind speeden_US
dc.titleMulti-time series and-time scale modeling for wind speed and wind power forecasting part I: Statistical methods, very short-term and short-term applicationsen_US
dc.typeConference Objecten_US

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