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

The 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 barri...

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Autores:
Rueda-Bayona, Juan Gabriel
Cabello Eras, Juan José
Sagastume, Alexis
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8574
Acceso en línea:
https://hdl.handle.net/11323/8574
https://doi.org/10.18280/mmep.080313
https://repositorio.cuc.edu.co/
Palabra clave:
Fourier analysis
Nonlinear autoregressive network
Wind potential
Reanalysis
Wind-speed
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_1b10dea44f40d1c618499088981ccf00
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8574
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
title Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
spellingShingle Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
Fourier analysis
Nonlinear autoregressive network
Wind potential
Reanalysis
Wind-speed
title_short Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
title_full Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
title_fullStr Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
title_full_unstemmed Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
title_sort Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model
dc.creator.fl_str_mv Rueda-Bayona, Juan Gabriel
Cabello Eras, Juan José
Sagastume, Alexis
dc.contributor.author.spa.fl_str_mv Rueda-Bayona, Juan Gabriel
Cabello Eras, Juan José
Sagastume, Alexis
dc.subject.spa.fl_str_mv Fourier analysis
Nonlinear autoregressive network
Wind potential
Reanalysis
Wind-speed
topic Fourier analysis
Nonlinear autoregressive network
Wind potential
Reanalysis
Wind-speed
description The 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.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-08-23T13:31:27Z
dc.date.available.none.fl_str_mv 2021-08-23T13:31:27Z
dc.date.issued.none.fl_str_mv 2021-05-18
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.issn.spa.fl_str_mv 2369-0739
2369-0747
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8574
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.18280/mmep.080313
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2369-0739
2369-0747
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8574
https://doi.org/10.18280/mmep.080313
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
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[9] Rueda-Bayona, J.G., Guzmán, A., Eras, J.J.C., SilvaCasarín, R., Bastidas-Arteaga, E., Horrillo-Caraballo, J. (2019). Renewables energies in Colombia and the opportunity for the offshore wind technology. Journal of Cleaner Production, 220: 529-543. https://doi.org/10.1016/j.jclepro.2019.02.174
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[28] Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., Feitosa, E. (2008). A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews, 12(6): 1725-1744. https://doi.org/10.1016/j.rser.2007.01.015
[29] Liu, H., Duan, Z., Wu, H., Li, Y., Dong, S. (2019). Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network. Measurement, 148: 106971. https://doi.org/10.1016/j.measurement.2019.106971
[30] Wang, H.Z., Li, G.Q., Wang, G.B., Peng, J.C., Jiang, H., Liu, Y.T. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied Energy, 188: 56-70. https://doi.org/10.1016/j.apenergy.2016.11.111
[31] Carvalho, D., Rocha, A., Santos, C.S., Pereira, R. (2013). Wind resource modelling in complex terrain using different mesoscale-microscale coupling techniques. Applied Energy, 108: 493-504. https://doi.org/10.1016/j.apenergy.2013.03.074
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[33] Han, Q., Ma, S., Wang, T., Chu, F. (2019). Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China. Renewable and Sustainable Energy Reviews, 115: 109387. https://doi.org/10.1016/j.rser.2019.109387
[34] Masseran, N. (2016). Modeling the fluctuations of wind speed data by considering their mean and volatility effects. Renewable and Sustainable Energy Reviews, 54: 777-784. https://doi.org/10.1016/j.rser.2015.10.071
[35] Zhao, J., Guo, Z.H., Su, Z.Y., Zhao, Z.Y., Xiao, X., Liu, F. (2016). An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Applied Energy, 162: 808-826. https://doi.org/10.1016/j.apenergy.2015.10.145
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spelling Rueda-Bayona, Juan GabrielCabello Eras, Juan JoséSagastume, Alexis2021-08-23T13:31:27Z2021-08-23T13:31:27Z2021-05-182369-07392369-0747https://hdl.handle.net/11323/8574https://doi.org/10.18280/mmep.080313Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.Rueda-Bayona, Juan Gabriel-will be generated-orcid-0000-0003-3806-2058-600Cabello Eras, Juan José-will be generated-orcid-0000-0003-0949-0862-600Sagastume, Alexis-will be generated-orcid-0000-0003-0188-7101-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mathematical Modelling of Engineering Problemshttps://www.iieta.org/journals/mmep/paper/10.18280/mmep.080313Fourier analysisNonlinear autoregressive networkWind potentialReanalysisWind-speedModeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network modelArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] British Petroleum, Statistical review of world energy, 2017. https://www.bp.com/content/dam/bp/businesssites/en/global/corporate/pdfs/news-and insights/speeches/bp-statistical-review-of-world-energy2017-lamar-mckay.pdf.[2] IPCC, Climate Change 2014: Mitigation of climate change, 3. 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Rec., 1989.[70] NOAA, NCEP North American Regional Reanalysis: NARR, (2016).PublicationORIGINALModeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model.pdfModeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model.pdfapplication/pdf1457197https://repositorio.cuc.edu.co/bitstreams/f69ff001-4ea6-4574-baea-c90c34b39e4b/download11bc5f51b72cf49694b27b4da6eddc70MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/fef888df-5bc0-441a-8292-c94b4ff1c2bc/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/62a09d0c-be90-4a2c-97fd-7204c290ffc6/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILModeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model.pdf.jpgModeling Wind Speed with a 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