Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level

Wind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasti...

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Detalles Bibliográficos
Autores: Manero, Jaume, Béjar, Javier, 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:2117/133571
Acceso en línea:https://hdl.handle.net/2117/133571
https://dx.doi.org/10.1088/1742-6596/1222/1/012037
Access Level:acceso abierto
Palabra clave:Wind power
Wind energy
Energy forecasting
Climate change
Energia eòlica
Àrees temàtiques de la UPC::Energies
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oai_identifier_str oai:upcommons.upc.edu:2117/133571
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spelling Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine levelManero, JaumeBéjar, JavierCortés García, Claudio Ulises|||0000-0003-0192-3096Wind powerWind energyEnergy forecastingClimate changeEnergia eòlicaÀrees temàtiques de la UPC::EnergiesWind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasting wind energy can be performed either by using weather forecast data or by projecting (or regressing) the past time-series data observations into the future. This last method is the statistical or time series approach. Wind Time Series show non-linearity and non-stationarity properties, and these two properties increase the complexity of the forecasting task using statistical methodologies. In this paper we explore the use of deep learning techniques, which can represent non-linearity, to the wind speed prediction using the largest public wind dataset available, the Wind Toolkit from the National Renewable Laboratory of the US. Several deep network architectures like Multi Layer Perceptrons, Convolutional Networks or Recurrent Networks have been tested on the 126,692 wind-sites and with the results obtained valuable comparisons and conclusions have been obtained. The distribution of the wind sites across the North American Geography has allowed to include in the analysis relationships between terrain, wind forecast complexity and deep methods. With the developed testing workbench and with the availability of the Barcelona Supercomputing Center new architectures are being developed. This work concludes with the feasibility of deep learning architectures for the wind and energy forecasting.We thank the Barcelona Supercomputing Center for the extensive use of their infrastructure in this project, and the United States National Renewable Energy Laboratory (NREL) for the use of their wind energy datasets. This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2014-SGR-1051)Peer ReviewedIOP Publishing20192019-05-0120192019-05-28journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/133571https://dx.doi.org/10.1088/1742-6596/1222/1/012037reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 SEV-2015-0493 BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACIONopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1335712026-05-27T15:37:01Z
dc.title.none.fl_str_mv Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
title Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
spellingShingle Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
Manero, Jaume
Wind power
Wind energy
Energy forecasting
Climate change
Energia eòlica
Àrees temàtiques de la UPC::Energies
title_short Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
title_full Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
title_fullStr Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
title_full_unstemmed Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
title_sort Deep Learning is blowing in the wind. Deep models applied to wind prediction at turbine level
dc.creator.none.fl_str_mv Manero, Jaume
Béjar, Javier
Cortés García, Claudio Ulises|||0000-0003-0192-3096
author Manero, Jaume
author_facet Manero, Jaume
Béjar, Javier
Cortés García, Claudio Ulises|||0000-0003-0192-3096
author_role author
author2 Béjar, Javier
Cortés García, Claudio Ulises|||0000-0003-0192-3096
author2_role author
author
dc.subject.none.fl_str_mv Wind power
Wind energy
Energy forecasting
Climate change
Energia eòlica
Àrees temàtiques de la UPC::Energies
topic Wind power
Wind energy
Energy forecasting
Climate change
Energia eòlica
Àrees temàtiques de la UPC::Energies
description Wind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasting wind energy can be performed either by using weather forecast data or by projecting (or regressing) the past time-series data observations into the future. This last method is the statistical or time series approach. Wind Time Series show non-linearity and non-stationarity properties, and these two properties increase the complexity of the forecasting task using statistical methodologies. In this paper we explore the use of deep learning techniques, which can represent non-linearity, to the wind speed prediction using the largest public wind dataset available, the Wind Toolkit from the National Renewable Laboratory of the US. Several deep network architectures like Multi Layer Perceptrons, Convolutional Networks or Recurrent Networks have been tested on the 126,692 wind-sites and with the results obtained valuable comparisons and conclusions have been obtained. The distribution of the wind sites across the North American Geography has allowed to include in the analysis relationships between terrain, wind forecast complexity and deep methods. With the developed testing workbench and with the availability of the Barcelona Supercomputing Center new architectures are being developed. This work concludes with the feasibility of deep learning architectures for the wind and energy forecasting.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-05-01
2019
2019-05-28
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/2117/133571
https://dx.doi.org/10.1088/1742-6596/1222/1/012037
url https://hdl.handle.net/2117/133571
https://dx.doi.org/10.1088/1742-6596/1222/1/012037
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 SEV-2015-0493 BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/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-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
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
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