Short term cloud nowcasting for a solar power plant based on irradiance historical Data

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dc.contributor.authorCaballero Roldán, Rafael
dc.contributor.authorZarzalejo Tirado, Luis Fernando
dc.contributor.authorOtero Martín, Álvaro
dc.contributor.authorPiñuel Moreno, Luis
dc.contributor.authorWilbert, Stefan
dc.date.accessioned2023-06-17T13:18:52Z
dc.date.available2023-06-17T13:18:52Z
dc.date.issued2018-12
dc.description© 2018 Universidad Nacional de La PLata This work has been partially supported by the Spanish MINECO project TIN2015-66471, and by the Santander-UCM project PR26/16-21B-1.
dc.description.abstractThis work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour. Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.
dc.description.departmentSección Deptal. de Arquitectura de Computadores y Automática (Físicas)
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Economía y Competitividad (MINECO)
dc.description.sponsorshipUniversidad Complutense de Madrid/Banco de Santander
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/50740
dc.identifier.doi10.24215/16666038.18.e21
dc.identifier.issn1666-6046
dc.identifier.officialurlhttp://dx.doi.org/10.24215/16666038.18.e21
dc.identifier.relatedurlhttp://journal.info.unlp.edu.ar
dc.identifier.urihttps://hdl.handle.net/20.500.14352/13009
dc.issue.number3
dc.journal.titleJournal of computer science & technology
dc.language.isoeng
dc.page.final192
dc.page.initial186
dc.publisherUniversidad Nacional de La Plata
dc.relation.projectIDTIN2015-66471
dc.relation.projectIDPR26/16-21B-1
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.cdu004.8
dc.subject.keywordTime-series
dc.subject.keywordRadiation
dc.subject.keywordCloud nowcasting
dc.subject.keywordGHI
dc.subject.keywordLSTM
dc.subject.keywordSupervised machine learning
dc.subject.keywordComputer Science
dc.subject.keywordArtificial Intelligence
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.unesco1203.04 Inteligencia Artificial
dc.titleShort term cloud nowcasting for a solar power plant based on irradiance historical Data
dc.typejournal article
dc.volume.number18
dspace.entity.typePublication
relation.isAuthorOfPublicationd17b0355-2695-449e-b06e-a34f4e27f120
relation.isAuthorOfPublication2ce782af-0e05-45eb-b58a-d2efffec6785
relation.isAuthorOfPublication.latestForDiscoveryd17b0355-2695-449e-b06e-a34f4e27f120
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