Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical app...

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Detalles Bibliográficos
Autores: Carreno Madinabeitia, Sheila, Ibarra Berastegi, Gabriel, Sáenz Aguirre, Jon, Zorita, Eduardo, Ulazia Manterola, Alain
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/63493
Acceso en línea:http://hdl.handle.net/10810/63493
Access Level:acceso abierto
Palabra clave:short-term forecast
wind
statistical forecast
random forest
ERA-Interim
Persistence
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spelling Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)Carreno Madinabeitia, SheilaIbarra Berastegi, GabrielSáenz Aguirre, JonZorita, EduardoUlazia Manterola, Alainshort-term forecastwindstatistical forecastrandom forestERA-InterimPersistenceThis study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts.This work was supported by the Spanish Government, MINECO project CGL2016-76561-R (MINECO/EU ERDF), and the University of the Basque Country (project GIU17/02).MDPI202320232019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/63493reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MINECO/CGL2016-76561-R/https://www.mdpi.com/2073-4433/11/1/45info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).oai:addi.ehu.eus:10810/634932026-06-18T09:23:17Z
dc.title.none.fl_str_mv Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
title Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
spellingShingle Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
Carreno Madinabeitia, Sheila
short-term forecast
wind
statistical forecast
random forest
ERA-Interim
Persistence
title_short Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
title_full Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
title_fullStr Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
title_full_unstemmed Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
title_sort Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)
dc.creator.none.fl_str_mv Carreno Madinabeitia, Sheila
Ibarra Berastegi, Gabriel
Sáenz Aguirre, Jon
Zorita, Eduardo
Ulazia Manterola, Alain
author Carreno Madinabeitia, Sheila
author_facet Carreno Madinabeitia, Sheila
Ibarra Berastegi, Gabriel
Sáenz Aguirre, Jon
Zorita, Eduardo
Ulazia Manterola, Alain
author_role author
author2 Ibarra Berastegi, Gabriel
Sáenz Aguirre, Jon
Zorita, Eduardo
Ulazia Manterola, Alain
author2_role author
author
author
author
dc.subject.none.fl_str_mv short-term forecast
wind
statistical forecast
random forest
ERA-Interim
Persistence
topic short-term forecast
wind
statistical forecast
random forest
ERA-Interim
Persistence
description This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts.
publishDate 2019
dc.date.none.fl_str_mv 2019
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/63493
url http://hdl.handle.net/10810/63493
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO/CGL2016-76561-R/
https://www.mdpi.com/2073-4433/11/1/45
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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