Short-term wind forecasting for energy resources scheduling
Main Author: | |
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Publication Date: | 2012 |
Other Authors: | , , |
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. |
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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 |
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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 |
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