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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP8W/3D7KBF8
Repositorysid.inpe.br/mtc-m18/2012/12.14.13.40   (restricted access)
Last Update2012:12.14.13.40.23 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m18/2012/12.14.13.40.23
Metadata Last Update2020:05.19.14.24.04 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN2218-6026
2304-3679
Citation KeyMartinsPereGuar:2012:SoRaFo
TitleSolar radiation forecast using artificial neural networks
ProjectFINEP (project 22.01.0569.00); PETROBRAS/CENPES (project 0050.0019.104.06.2); CNPq (grants 151700/2005-2, 141844/2006-0, 132148/2004-8, 555764/2010-9).
Year2012
MonthDec.
Access Date2024, May 16
Secondary TypePRE PI
Number of Files1
Size559 KiB
2. Context
Author1 Martins, Fernando Ramos
2 Pereira, Enio Bueno
3 Guarnieri, Ricardo André
Resume Identifier1
2 8JMKD3MGP5W/3C9JH2E
Group1 SCE-CST-INPE-MCTI-GOV-BR
2 CST-CST-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE/CPTEC)
2 Instituto Nacional de Pesquisas Espaciais (INPE/CPTEC)
3 Instituto Nacional de Pesquisas Espaciais (INPE/CPTEC)
Author e-Mail Address1 fernando.martins@inpe.br
JournalInternational Journal of Energy Science
Volume2
Number6
Pages217-227
ProgressePrint update
History (UTC)2012-12-14 13:43:09 :: deicy -> administrator :: 2012
2020-05-19 14:24:04 :: administrator -> simone :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordssolar nergy forecast
short-term forecast
artificial neural network
energy meteorology
AbstractThe fast increase in importance of the solar energy resource as viable and promising source of renewable energy has boosted research in methods to evaluate the short-term forecasts of the solar energy resource. There is an increase on demand from the energy sector for accurate short-term forecasts of solar energy resources in order to support the planning and management of the electricity generation and distribution systems. The Eta model is the mesoscale model running at CPTEC/INPE for weather forecasts and climate studies. It provides outputs for solar radiation flux at the surface, but these solar radiation forecasts are greatly overestimated. In order to achieve more reliable information, Artificial Neural Networks (ANN) were used to refine short-term forecast for the downward solar radiation flux at the surfaceprovided by Eta/CPTEC model. Ground measurements of downward solar radiation flux acquired in two SONDA sites located in Southern region of Brazil (Florianópolis and São Martinho da Serra) were used for ANN training and validation. The short-term forecasts produced by ANN have presented higher correlation coefficients and lower deviations. The ANN removed the bias observed in solar radiation forecasts provided by Eta/CPTEC model. The skill improvement in RMSE was higher than 30%when ANN was used to provide short-term forecasts of solar radiation at the surface in both measurement sites.
AreaMET
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Solar radiation forecast...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target FileSolar Radiation Forecast Using Artificial Neural Networks.pdf
User Groupadministrator
deicy
Reader Groupadministrator
deicy
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Previous Editionsid.inpe.br/mtc-m18@80/2008/07.22.19.39
Next Higher Units8JMKD3MGPCW/3F3T29H
Citing Item Listsid.inpe.br/bibdigital/2013/10.19.20.40 2
sid.inpe.br/mtc-m21/2012/07.13.14.45.21 1
DisseminationWEBSCI; PORTALCAPES.
Host Collectionsid.inpe.br/mtc-m18@80/2008/03.17.15.17
6. Notes
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