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1. Identity statement
Reference TypeReport
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/44CUAMH
Repositorysid.inpe.br/mtc-m21c/2021/03.24.17.22
Last Update2021:03.24.17.22.16 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2021/03.24.17.22.16
Metadata Last Update2022:04.03.22.29.36 (UTC) administrator
Citation KeyMartinsCereMantWang:2021:SyLiRe
TitleSystematic Literature Review on Forecasting/Nowcasting based upon Ground-Based Cloud Imaging
Year2021
Access Date2024, May 19
TypeRPQ
Number of Pages64
Number of Files1
Size24952 KiB
2. Context
Author1 Martins, Bruno Juncklaus
2 Cerentini, Allan
3 Mantelli Neto, Sylvio Luiz
4 von Wangenheim, Aldo
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JJ9K
Group1
2
3 DIIAV-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Universidade Federal de Santa Catarina (UFSC)
2 Universidade Federal de Santa Catarina (UFSC)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Universidade Federal de Santa Catarina (UFSC)
Author e-Mail Address1 bjuncklaus@gmail.com
2 allancerentini@gmail.com
3 sylvio@lepten.ufsc.br
4 aldo.vw@ufsc.br
InstitutionInstituto Nacional de Pesquisas Espaciais
CitySão José dos Campos
History (UTC)2021-03-24 17:22:40 :: simone :: -> 2021
2021-03-24 17:23:29 :: simone -> administrator :: 2021
2022-04-03 22:29:36 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsArtificial neural networks
forecasting
cloud imaging
AbstractArtificial Neural Networks (ANN) are being used on several fields mostly as a mapper from input domain variables into output application area results. Several methods are being used on the automatic assessment of clouds from surface to predict solar power generation, assisted by a camera, side sensors, etc. The present Systematic Literature Review (SLR) is intended to search the related scientific articles, to find the state of the art in the area. We were able to find gaps in researches in regards to validation metrics for prediction of solar power generation as well as a small number of works in this domain area using computational intelligence (machine learning) methods, with the majority of works relying on classical statistics approaches. Results show that most works rely on images captured by Total Sky-imagers (TSI) and most works using computational intelligence rely on classical approaches like Artificial Neural Networks, Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP) and that there still a relevant amount of works published from the last three years using classical statistics.
AreaCST
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Systematic Literature Review...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://mtc-m21c.sid.inpe.br/ibi/8JMKD3MGP3W34R/44CUAMH
zipped data URLhttp://mtc-m21c.sid.inpe.br/zip/8JMKD3MGP3W34R/44CUAMH
Languageen
Target FileMartins_systematic.pdf
User Groupsimone
Visibilityshown
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.15.00.44 1
DisseminationBNDEPOSITOLEGAL
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber contenttype copyholder copyright creatorhistory date descriptionlevel doi e-mailaddress edition format isbn issn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup recipient reportnumber rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype session shorttitle sponsor subject tertiarymark tertiarytype translator url versiontype
7. Description control
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