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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3PJDG4H
Repositorysid.inpe.br/mtc-m21b/2017/09.06.15.43   (restricted access)
Last Update2017:09.06.15.43.39 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m21b/2017/09.06.15.43.39
Metadata Last Update2021:01.02.03.54.12 (UTC) administrator
DOI10.1007/s40313-017-0329-8
ISSN21953880
21953899
Citation KeyLimaGueFrePanMat:2017:MeMoSh
TitleA meteorological–statistic model for short-term wind power forecasting
Year2017
MonthOct.
Access Date2024, Mar. 29
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1286 KiB
2. Context
Author1 Lima, João Marcos
2 Guetter, Alexandre K.
3 Freitas, Saulo R.
4 Panetta, Jairo
5 Mattos, João Gerd Zell de
Group1
2
3
4
5 DIDMD-CGCPT-INPE-MCTIC-GOV-BR
Affiliation1 Copel Geração e Transmissão S.A
2 Universidade Federal do Paraná (UFPR)
3 USRA/GESTAR at NASA/GFSC
4 Instituto Tecnológico de Aeronáutica (ITA)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 jm.lima@copel.com
2 guetter.dhs@ufpr.br
3 saulo.r.freitas@nasa.gov
4 jairo.panetta@gmail.com
5 joao.gerd@inpe.br
JournalJournal of Control, Automation and Electrical Systems
Volume28
Number5
Pages679-691
History (UTC)2017-09-06 15:44:24 :: simone -> administrator :: 2017
2017-11-09 13:11:42 :: administrator -> simone :: 2017
2017-12-11 17:00:51 :: simone -> administrator :: 2017
2021-01-02 03:54:12 :: administrator -> simone :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsWind power forecast · Numerical weather prediction models · Kalman filtering · Power curve
AbstractThe problem of wind power forecasting is addressed in this work, considering a combination of a numerical weather prediction model (NWP) and statistical models. Brazilian developments on the Regional Atmospheric Modeling System is employed in two different areas in Brazil to simulate forecasts of 72 h ahead of the wind speed, at each 10 min. In one of the areas studied, the wind speed is converted into wind power. Different conversion methods are employed and discussed. Kalman filtering techniques are employed to reduce systematic error of the forecasts, both wind and generation. Each 72-h period of the NWP simulations had a computational time of approximately 6070 min using indicating that the proposed method can be applied in real time for power system operation. The results obtained are very encouraging for further investigation to achieve more accurate wind power researches.
AreaMET
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMD > A meteorological–statistic model...
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4. Conditions of access and use
Languageen
Target Filelima_meteorological.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Linking8JMKD3MGP3W34P/3L9KS8B
Mirror Repositorysid.inpe.br/mtc-m21b/2013/09.26.14.25.22
Next Higher Units8JMKD3MGPCW/43SKC35
Citing Item Listsid.inpe.br/bibdigital/2021/01.01.17.20 1
DisseminationWEBSCI; COMPENDEX; SCOPUS.
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
Empty Fieldsalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarytype url
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