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An artificial neural network approach for short-term wind power forecasting in Portugal

Bibliographic Details
Main Author: Catalão, João Paulo da Silva
Publication Date: 2009
Other Authors: Pousinho, Hugo Miguel Inácio, Mendes, Victor
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.21/1272
Summary: This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.
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spelling An artificial neural network approach for short-term wind power forecasting in PortugalArtificial neural networksForecastingWind powerModelsPredictionSpeedThis paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.C R L Publishing LTDRCIPLCatalão, João Paulo da SilvaPousinho, Hugo Miguel InácioMendes, Victor2012-03-13T13:05:19Z2009-032009-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://hdl.handle.net/10400.21/1272eng1472-8915info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-12T08:50:51Zoai:repositorio.ipl.pt:10400.21/1272Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:57:49.541307Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv An artificial neural network approach for short-term wind power forecasting in Portugal
title An artificial neural network approach for short-term wind power forecasting in Portugal
spellingShingle An artificial neural network approach for short-term wind power forecasting in Portugal
Catalão, João Paulo da Silva
Artificial neural networks
Forecasting
Wind power
Models
Prediction
Speed
title_short An artificial neural network approach for short-term wind power forecasting in Portugal
title_full An artificial neural network approach for short-term wind power forecasting in Portugal
title_fullStr An artificial neural network approach for short-term wind power forecasting in Portugal
title_full_unstemmed An artificial neural network approach for short-term wind power forecasting in Portugal
title_sort An artificial neural network approach for short-term wind power forecasting in Portugal
author Catalão, João Paulo da Silva
author_facet Catalão, João Paulo da Silva
Pousinho, Hugo Miguel Inácio
Mendes, Victor
author_role author
author2 Pousinho, Hugo Miguel Inácio
Mendes, Victor
author2_role author
author
dc.contributor.none.fl_str_mv RCIPL
dc.contributor.author.fl_str_mv Catalão, João Paulo da Silva
Pousinho, Hugo Miguel Inácio
Mendes, Victor
dc.subject.por.fl_str_mv Artificial neural networks
Forecasting
Wind power
Models
Prediction
Speed
topic Artificial neural networks
Forecasting
Wind power
Models
Prediction
Speed
description This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.
publishDate 2009
dc.date.none.fl_str_mv 2009-03
2009-03-01T00:00:00Z
2012-03-13T13:05:19Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.21/1272
url http://hdl.handle.net/10400.21/1272
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1472-8915
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv C R L Publishing LTD
publisher.none.fl_str_mv C R L Publishing LTD
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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