Short-term wind forecasting for energy resources scheduling

Bibliographic Details
Main Author: Ramos, Sérgio
Publication Date: 2012
Other Authors: Soares, João, Silva, Marco, Vale, Zita
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.22/1444
Summary: This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.
id RCAP_ece1880a0081d4278206760eea99cc91
oai_identifier_str oai:recipp.ipp.pt:10400.22/1444
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Short-term wind forecasting for energy resources schedulingArtificial neural networkWind power and speed forecastingShort - term forecastingShort - term schedulingThis paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.EWEAREPOSITÓRIO P.PORTORamos, SérgioSoares, JoãoSilva, MarcoVale, Zita2013-04-22T11:08:37Z20122013-04-11T14:04:53Z2012-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.22/1444enginfo: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:RCAAP2025-04-02T02:53:35Zoai:recipp.ipp.pt:10400.22/1444Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T00:26:45.416156Repositórios 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 Short-term wind forecasting for energy resources scheduling
title Short-term wind forecasting for energy resources scheduling
spellingShingle Short-term wind forecasting for energy resources scheduling
Ramos, Sérgio
Artificial neural network
Wind power and speed forecasting
Short - term forecasting
Short - term scheduling
title_short Short-term wind forecasting for energy resources scheduling
title_full Short-term wind forecasting for energy resources scheduling
title_fullStr Short-term wind forecasting for energy resources scheduling
title_full_unstemmed Short-term wind forecasting for energy resources scheduling
title_sort Short-term wind forecasting for energy resources scheduling
author Ramos, Sérgio
author_facet Ramos, Sérgio
Soares, João
Silva, Marco
Vale, Zita
author_role author
author2 Soares, João
Silva, Marco
Vale, Zita
author2_role author
author
author
dc.contributor.none.fl_str_mv REPOSITÓRIO P.PORTO
dc.contributor.author.fl_str_mv Ramos, Sérgio
Soares, João
Silva, Marco
Vale, Zita
dc.subject.por.fl_str_mv Artificial neural network
Wind power and speed forecasting
Short - term forecasting
Short - term scheduling
topic Artificial neural network
Wind power and speed forecasting
Short - term forecasting
Short - term scheduling
description This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
2013-04-22T11:08:37Z
2013-04-11T14:04:53Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/1444
url http://hdl.handle.net/10400.22/1444
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv EWEA
publisher.none.fl_str_mv EWEA
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
_version_ 1833600537129385984