Deep learning-based forecasting of aggregated CSP production
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Otros documentos de la autoría: Segarra-Tamarit, Jorge; Pérez, Emilio; Moya Bueno, Eric; Ayuso, Pablo; Beltrán San Segundo, Héctor
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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INVESTIGACIONMetadatos
Título
Deep learning-based forecasting of aggregated CSP productionAutoría
Fecha de publicación
2020-02-25Editor
ElsevierISSN
0378-4754Cita bibliográfica
J. Segarra-Tamarit, E. Pérez, E. Moya et al., Deep learning-based forecasting of aggregated CSP production, Mathematics and Computers in Simulation (2020), https://doi.org/10.1016/j.matcom.2020.02.007.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S037847542030046XVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere ... [+]
This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance
values and available weather conditions forecasts for the locations where the main CSP power plants are installed.
The performances of the forecast models are analysed and compared by means of the most extended metrics in the
literature for a whole year of CSP energy production. [-]
Publicado en
Mathematics and Computers in Simulation (2020).Proyecto de investigación
JI-B2017-26, ACIF/2019/106Derechos de acceso
0378-4754/© 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rightsreserved
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info:eu-repo/semantics/openAccess
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info:eu-repo/semantics/openAccess
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