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Multistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selection

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dc.citedby21
dc.contributor.authorNoman F.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorAl-Shetwi A.Q.en_US
dc.contributor.authorKiong Tiong S.en_US
dc.contributor.authorAlalwan N.en_US
dc.contributor.authorEkanayake J.en_US
dc.contributor.authorAlzahrani A.I.en_US
dc.contributor.authorid55327881300en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57004922700en_US
dc.contributor.authorid57219799117en_US
dc.contributor.authorid35309472000en_US
dc.contributor.authorid7003409510en_US
dc.contributor.authorid54912750300en_US
dc.date.accessioned2023-05-29T09:09:16Z
dc.date.available2023-05-29T09:09:16Z
dc.date.issued2021
dc.descriptionElectric power transmission networks; Forecasting; Mean square error; Neural networks; Predictive analytics; Speed; Transfer learning; Wind; Wind power; Exogenous variables; Multi-step prediction; Neural networks (NNS); Root mean square errors; Short-term wind speed predictions; Transfer learning methods; Variable selection methods; Wind speed prediction; Learning systemsen_US
dc.description.abstractPrecise wind speed prediction is a key factor in many energy applications, especially when wind energy is integrated with power grids. However, because of the intermittent and nonstationary nature of wind speed, modeling and predicting it is a challenge. In addition, using uncorrelated multivariate variables as exogenous input variables often adversely impacts the performance of prediction models. In this paper, we present a multistep short-term wind speed prediction using multivariate exogenous input variables. We implement different variable selection methods to select the best set of variables that significantly improve the performance of prediction models. We evaluate the performance of eight transfer learning methods, four shallow neural networks (NNs), and the persistence method on predicting the future values of wind speed using ultrashort-term, short-term, and multistep time horizons. We performed the evaluation over two-year high-sampled wind speed data averaged at 10-minute intervals. Results show that Nonlinear Auto-Regressive Exogenous (NARX) model outperformed all other methods, achieving an average mean absolute error (MAE) and root mean square error (RMSE) of 0.2205 and 0.3405 for multistep predictions, respectively. Despite the lower performance of the transfer learning methods (i.e., 0.43 and 0.58 for MAE and RMSE, respectively), it is believed that results could be further improved with a better enhancement of the feature selection and model parameters. � 2020 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2020.10.045
dc.identifier.epage1229
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85095564934
dc.identifier.spage1221
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85095564934&doi=10.1016%2fj.aej.2020.10.045&partnerID=40&md5=c8ede2d5f6c7fd8f0ba6836dc4c7a976
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26337
dc.identifier.volume60
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.titleMultistep short-term wind speed prediction using nonlinear auto-regressive neural network with exogenous variable selectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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