Publication:
Wind speed prediction over Malaysia using various machine learning models: potential renewable energy source

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dc.citedby1
dc.contributor.authorHanoon M.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorRazzaq A.en_US
dc.contributor.authorZaini N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorChau K.W.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57266877500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid57219410567en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:40:01Z
dc.date.available2023-05-29T09:40:01Z
dc.date.issued2022
dc.description.abstractModeling wind speed has a signi?cant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches�Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR)�were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2022.2103588
dc.identifier.epage1689
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85136223863
dc.identifier.spage1673
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136223863&doi=10.1080%2f19942060.2022.2103588&partnerID=40&md5=9407edef261647156c1c2963089c7697
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27134
dc.identifier.volume16
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleWind speed prediction over Malaysia using various machine learning models: potential renewable energy sourceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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