Data-Adaptive Censoring for Short-Term Wind Speed Predictors Based on MLP, RNN, and SVM

Basit Öğe Kaydı
dc.authoridpeker, murat/0000-0001-9877-5493
dc.contributor.authorSarp, Ali Ogun
dc.contributor.authorMenguc, Engin Cemal
dc.contributor.authorPeker, Murat
dc.contributor.authorGuvenc, Buket Colak
dc.date.accessioned2024-11-07T13:32:45Z
dc.date.available2024-11-07T13:32:45Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThis study introduces novel short-term wind speed predictors based on multilayer perceptron (MLP), recurrent neural network (RNN), and support vector machine (SVM) by combining them with the data-adaptive censoring (DAC) strategy. Taking into account the multistep ahead prediction mode, we design a DAC strategy based on the least mean square (LMS) algorithm, which iteratively obtains a new training dataset consisting of the most informative input-output wind data from all training set for MLP, RNN, and SVM structures. This enables us to censor less informative training data with high accuracy and thereby the training costs of the MLP, RNN, and SVM are reduced without a considerably adverse effect on their prediction performances in testing processes. The conducted simulation results on real-life large-scale short-term wind speed data verify the mentioned attractive features of the proposed predictors.
dc.identifier.doi10.1109/JSYST.2022.3150749
dc.identifier.endpage3634
dc.identifier.issn1932-8184
dc.identifier.issn1937-9234
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85125748706
dc.identifier.scopusqualityQ1
dc.identifier.startpage3625
dc.identifier.urihttps://doi.org/10.1109/JSYST.2022.3150749
dc.identifier.urihttps://hdl.handle.net/11480/15596
dc.identifier.volume16
dc.identifier.wosWOS:000764849100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Systems Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectTraining
dc.subjectWind speed
dc.subjectPrediction algorithms
dc.subjectSupport vector machines
dc.subjectPredictive models
dc.subjectWind farms
dc.subjectTesting
dc.subjectData-adaptive censoring (DAC)
dc.subjectleast mean square (LMS)
dc.subjectmultilayer perceptron (MLP)
dc.subjectrecurrent neural networks (RNNs)
dc.subjectsupport vector machine (SVM)
dc.subjectwind speed
dc.titleData-Adaptive Censoring for Short-Term Wind Speed Predictors Based on MLP, RNN, and SVM
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu