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A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant
dc.contributor.author | Pérez, Emilio | |
dc.contributor.author | Pérez Soler, Javier | |
dc.contributor.author | Segarra-Tamarit, Jorge | |
dc.contributor.author | Beltran, Hector | |
dc.date.accessioned | 2022-01-19T12:34:21Z | |
dc.date.available | 2022-01-19T12:34:21Z | |
dc.date.issued | 2021-03-25 | |
dc.identifier.citation | 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. | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/196483 | |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 9 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | Solar Energy, Vol. 218, April 2021 | ca_CA |
dc.rights | © 2021 International Solar Energy Society. Published by Elsevier Ltd. All rights reserved. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | irradiance forecasting | ca_CA |
dc.subject | deep learning | ca_CA |
dc.subject | neural networks | ca_CA |
dc.subject | satellite data | ca_CA |
dc.title | A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1016/j.solener.2021.02.033 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | European Social Fund (ESF) | ca_CA |
oaire.awardNumber | UJI-B2017-26 | ca_CA |
oaire.awardNumber | ACIF/2019/106 | ca_CA |
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