A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant
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Otros documentos de la autoría: Pérez, Emilio; Pérez Soler, Javier; Segarra-Tamarit, Jorge; Beltran, Hector
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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Título
A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plantFecha de publicación
2021-03-25Editor
ElsevierCita bibliográfica
PÉREZ, Emilio, et al. A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant. Solar Energy, 2021, vol. 218, p. 652-660.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
This work proposes an intra-day forecasting model, which does not require to be trained or fed with real-time data measurements, for global horizontal irradiance (GHI) at a given location. The proposed model uses a ... [+]
This work proposes an intra-day forecasting model, which does not require to be trained or fed with real-time data measurements, for global horizontal irradiance (GHI) at a given location. The proposed model uses a series of time-dependant irradiance estimates near the target location as the main input. These estimates are derived from satellite images and are combined with other secondary inputs in an advanced neural network, which features convolutional and dense layers and is trained using a deep learning approach. For the various input combinations, the performance of the model is validated with a quantitative analysis on the forecast accuracy using different error metrics. Accuracies are compared with a commercial solution for irradiance forecasting made by the European Centre for Medium-Range Weather Forecasts (ECMWF) and publications with similar approaches and forecasting horizons, showing state-of-the-art performance even without irradiance measurements. [-]
Publicado en
Solar Energy, Vol. 218, April 2021Entidad financiadora
Universitat Jaume I | Generalitat Valenciana | European Social Fund (ESF)
Código del proyecto o subvención
UJI-B2017-26 | ACIF/2019/106
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© 2021 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved.
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info:eu-repo/semantics/openAccess
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