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1. Identity statement
Reference TypeePrint (Electronic Source)
Sitemtc-m16c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZGivnK3Y/UQFBP
Repositorysid.inpe.br/mtc-m18@80/2008/07.22.19.39
Last Update2008:07.22.19.39.58 (UTC) deicy
Metadata Repositorysid.inpe.br/mtc-m18@80/2008/07.22.19.39.54
Metadata Last Update2022:03.26.17.50.16 (UTC) administrator
Citation KeyGuarnieriMartPereChan:2008:SoRaFo
TitleSolar radiation forecast using artificial neural networks
Year2008
Access Date2024, May 16
Type of MediumOn-line
Secondary TypePRE PI
Number of Files1
Size1074 KiB
2. Context
Author1 Guarnieri, Ricardo André
2 Martins, Fernando Ramos
3 Pereira, Enio Bueno
4 Chan, Chou Sin
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JH2E
4 8JMKD3MGP5W/3C9JGRQ
Group1 DMA-CPT-INPE-MCT-BR
2 DMA-CPT-INPE-MCT-BR
3 DMA-CPT-INPE-MCT-BR
4 DMD-CPT-INPE-MCT-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)
4 Instituto Nacional de Pesquisas Espaciais (INPE/CPTEC)
Alternate PublicationJournal Applied Meteorological and Climatology
ProducerInstituto Nacional de Pesquisas Espaciais
CitySão José dos Campos
Stage of Alternate Publicationpublished
ProgressePrint update
History (UTC)2008-07-22 19:39:58 :: deicy -> administrator ::
2012-07-18 00:04:55 :: administrator -> deicy ::
2012-12-14 13:40:23 :: deicy -> administrator :: -> 2008
2022-03-26 17:50:16 :: administrator -> simone :: 2008
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordssolar radiation
climate
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 increasing demand from the energy sector for accurate forecasts of solar energy resources in order to support the planning and management of the electricity generation and distribution systems. The Eta model that runs operationally in the Brazilian Center of Weather Forecast and Climate Studies (CPTEC/INPE) has outputs for solar radiation flux at the surface. However, these radiation forecasts are greatly overestimated. In order to achieve more reliable short-term estimates for the downward solar radiation flux at the surface, forecasts for the future atmospheric conditions provided by Eta/CPTEC model were used as inputs in Artificial Neural Networks (ANNs). Ground data of downward solar radiation flux acquired in two SONDA sites located in south Brazil (Florianópolis and São Martinho da Serra) were used for ANNs training and for forecasts evaluation. The forecasts from ANNs have presented higher correlation coefficients and lower deviations than the model Eta/CPTEC output. The bias observed in solar radiation forecasts provided by the Eta/CPTEC model was removed by the ANNs. The improvement in RMSE obtained with ANNs over the model Eta/CPTEC was higher than 30%.
AreaMET
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMD > Solar radiation forecast...
Arrangement 2urlib.net > BDMCI > Fonds > Produção até 2016 > DMA > Solar radiation forecast...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZGivnK3Y/UQFBP
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZGivnK3Y/UQFBP
Languageen
Target Filev1.pdf
User Groupadministrator
deicy
marciana
Visibilityshown
5. Allied materials
Next Editionsid.inpe.br/mtc-m18/2012/12.14.13.40
Next Higher Units8JMKD3MGPCW/43SKC35
8JMKD3MGPCW/46JKC45
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.45.21 4
Host Collectionsid.inpe.br/mtc-m18@80/2008/03.17.15.17
6. Notes
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