“Dust in the wind...”, deep learning application to wind energy time series forecasting

To 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 electri...

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Detalles Bibliográficos
Autores: Manero Font, Jaume, Béjar Alonso, Javier|||0000-0001-5281-3888, Cortés García, Claudio Ulises|||0000-0003-0192-3096
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/166842
Acceso en línea:https://hdl.handle.net/2117/166842
https://dx.doi.org/10.3390/en12122385
Access Level:acceso abierto
Palabra clave:Weather forecasting
Machine learning
Wind power
Wind energy forecasting
Time series
Deep learning
RNN
MLP
CNN
Wind speed forecasting
Wind time series
Multi-step forecasting
Previsió del temps
Aprenentatge automàtic
Energia eòlica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia eòlica
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oai_identifier_str oai:upcommons.upc.edu:2117/166842
network_acronym_str ES
network_name_str España
repository_id_str
spelling “Dust in the wind...”, deep learning application to wind energy time series forecastingManero Font, JaumeBéjar Alonso, Javier|||0000-0001-5281-3888Cortés García, Claudio Ulises|||0000-0003-0192-3096Weather forecastingMachine learningWind powerWind energy forecastingTime seriesDeep learningRNNMLPCNNWind speed forecastingWind time seriesTime seriesMulti-step forecastingPrevisió del tempsAprenentatge automàticEnergia eòlicaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticÀrees temàtiques de la UPC::Energies::Energia eòlicaTo 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.Peer Reviewed20192019-06-2120192019-07-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/166842https://dx.doi.org/10.3390/en12122385reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2015-65316-P COMPUTACION DE ALTAS PRESTACIONES VIIopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1668422026-05-27T15:37:01Z
dc.title.none.fl_str_mv “Dust in the wind...”, deep learning application to wind energy time series forecasting
title “Dust in the wind...”, deep learning application to wind energy time series forecasting
spellingShingle “Dust in the wind...”, deep learning application to wind energy time series forecasting
Manero Font, Jaume
Weather forecasting
Machine learning
Wind power
Wind energy forecasting
Time series
Deep learning
RNN
MLP
CNN
Wind speed forecasting
Wind time series
Time series
Multi-step forecasting
Previsió del temps
Aprenentatge automàtic
Energia eòlica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia eòlica
title_short “Dust in the wind...”, deep learning application to wind energy time series forecasting
title_full “Dust in the wind...”, deep learning application to wind energy time series forecasting
title_fullStr “Dust in the wind...”, deep learning application to wind energy time series forecasting
title_full_unstemmed “Dust in the wind...”, deep learning application to wind energy time series forecasting
title_sort “Dust in the wind...”, deep learning application to wind energy time series forecasting
dc.creator.none.fl_str_mv Manero Font, Jaume
Béjar Alonso, Javier|||0000-0001-5281-3888
Cortés García, Claudio Ulises|||0000-0003-0192-3096
author Manero Font, Jaume
author_facet Manero Font, Jaume
Béjar Alonso, Javier|||0000-0001-5281-3888
Cortés García, Claudio Ulises|||0000-0003-0192-3096
author_role author
author2 Béjar Alonso, Javier|||0000-0001-5281-3888
Cortés García, Claudio Ulises|||0000-0003-0192-3096
author2_role author
author
dc.subject.none.fl_str_mv Weather forecasting
Machine learning
Wind power
Wind energy forecasting
Time series
Deep learning
RNN
MLP
CNN
Wind speed forecasting
Wind time series
Time series
Multi-step forecasting
Previsió del temps
Aprenentatge automàtic
Energia eòlica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia eòlica
topic Weather forecasting
Machine learning
Wind power
Wind energy forecasting
Time series
Deep learning
RNN
MLP
CNN
Wind speed forecasting
Wind time series
Time series
Multi-step forecasting
Previsió del temps
Aprenentatge automàtic
Energia eòlica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Energies::Energia eòlica
description To 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-06-21
2019
2019-07-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/166842
https://dx.doi.org/10.3390/en12122385
url https://hdl.handle.net/2117/166842
https://dx.doi.org/10.3390/en12122385
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 TIN2015-65316-P COMPUTACION DE ALTAS PRESTACIONES VII
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869416001550942208
score 15,198674

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