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dc.contributor.authorManero Font, Jaume
dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2019-07-25T07:04:33Z
dc.date.available2019-07-25T07:04:33Z
dc.date.issued2019-06-21
dc.identifier.citationManero, J.; Béjar, J.; Cortés, U. “Dust in the wind...”, deep learning application to wind energy time series forecasting. "Energies", 21 Juny 2019, vol. 12, núm. 12, p. 1-20.
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/2117/166842
dc.description.abstractTo balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.
dc.format.extent20 p.
dc.language.isoeng
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subjectÀrees temàtiques de la UPC::Energies::Energia eòlica
dc.subject.lcshWeather forecasting
dc.subject.lcshMachine learning
dc.subject.lcshWind power
dc.subject.otherWind energy forecasting
dc.subject.otherTime series
dc.subject.otherDeep learning
dc.subject.otherRNN
dc.subject.otherMLP
dc.subject.otherCNN
dc.subject.otherWind speed forecasting
dc.subject.otherWind time series
dc.subject.otherTime series
dc.subject.otherMulti-step forecasting
dc.title“Dust in the wind...”, deep learning application to wind energy time series forecasting
dc.typeArticle
dc.subject.lemacPrevisió del temps
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacEnergia eòlica
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.3390/en12122385
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/1996-1073/12/12/2385
dc.rights.accessOpen Access
drac.iddocument25639600
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/V PRI/2014 SGR 1051
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
upcommons.citation.authorManero, J.; Béjar, J.; Cortés, U.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameEnergies
upcommons.citation.volume12
upcommons.citation.number12
upcommons.citation.startingPage1
upcommons.citation.endingPage20


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution 3.0 Spain