Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model

dc.creatorRueda-Bayona, Juan Gabriel
dc.creatorCabello Eras, Juan José
dc.creatorSagastume, Alexis
dc.date2021-08-23T13:31:27Z
dc.date2021-08-23T13:31:27Z
dc.date2021-05-18
dc.date.accessioned2023-10-03T19:11:50Z
dc.date.available2023-10-03T19:11:50Z
dc.identifier2369-0739
dc.identifier2369-0747
dc.identifierhttps://hdl.handle.net/11323/8574
dc.identifierhttps://doi.org/10.18280/mmep.080313
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168771
dc.descriptionThe limited availability of local climatological stations and the limitations to predict the wind speed (WS) accurately are significant barriers to the expansion of wind energy (WE) projects worldwide. A methodology to forecast accurately the WS at the local scale can be used to overcome these barriers. This study proposes a methodology to forecast the WS with high-resolution and long-term horizons, which combines a Fourier model and a nonlinear autoregressive network (NAR). Given the nonlinearities of the WS variations, a NAR model is used to forecast the WS based on the variability identified with the Fourier analysis. The NAR modelled successfully 1.7 years of windspeed with 3 hours of the time interval, what may be considered the longest forecasting horizon with high resolution at the moment.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceMathematical Modelling of Engineering Problems
dc.sourcehttps://www.iieta.org/journals/mmep/paper/10.18280/mmep.080313
dc.subjectFourier analysis
dc.subjectNonlinear autoregressive network
dc.subjectWind potential
dc.subjectReanalysis
dc.subjectWind-speed
dc.titleModeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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