Hybrid boosting algorithms and artificial neural network for wind speed prediction

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dc.authoridBoru Ipek, Asli/0000-0001-6403-5307
dc.authoridDosdogru, Ayse Tugba/0000-0002-1548-5237
dc.contributor.authorDosdogru, Ayse Tugba
dc.contributor.authorIpek, Asli Boru
dc.date.accessioned2025-01-06T17:37:48Z
dc.date.available2025-01-06T17:37:48Z
dc.date.issued2022
dc.description.abstractEnergy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this point, wind energy can be used as one of the key source of renewable energy. It has a remarkable importance among the low-carbon energy technologies. The primary aim of wind energy production is to reduce dependence on fossil fuels that affect environment adversely. Therefore, wind energy is analyzed to develop new energy resources. The main issue related to evaluation of the wind energy potential is wind speed prediction. Due to the high volatile and irregular nature of wind speed, wind speed prediction is difficult. To cope with complex data structure, this study presents the development of extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and artificial neural network (ANN) within particle swarm optimization (PSO) parameter optimization for hourly wind speed prediction. To compare the proposed hybrid methods, various performance measures, the Pearson's test, and the Taylor diagram are used. The results showed that proposed hybrid methods provide reasonable prediction results for wind speed prediction. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.ijhydene.2021.10.154
dc.identifier.endpage1460
dc.identifier.issn0360-3199
dc.identifier.issn1879-3487
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85119271078
dc.identifier.scopusqualityQ1
dc.identifier.startpage1449
dc.identifier.urihttps://doi.org/10.1016/j.ijhydene.2021.10.154
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2367
dc.identifier.volume47
dc.identifier.wosWOS:000737956900004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofInternational Journal of Hydrogen Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectExtreme gradient boosting
dc.subjectAdaptive boosting
dc.subjectArtificial neural network
dc.subjectParticle swarm optimization
dc.subjectWind speed prediction
dc.titleHybrid boosting algorithms and artificial neural network for wind speed prediction
dc.typeArticle

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