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Deep learning-based forecasting of aggregated CSP production
dc.contributor.author | Segarra-Tamarit, Jorge | |
dc.contributor.author | Pérez, Emilio | |
dc.contributor.author | Moya Bueno, Eric | |
dc.contributor.author | Ayuso, Pablo | |
dc.contributor.author | Beltrán San Segundo, Héctor | |
dc.date.accessioned | 2020-10-21T10:11:50Z | |
dc.date.available | 2020-10-21T10:11:50Z | |
dc.date.issued | 2020-02-25 | |
dc.identifier.citation | 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. | ca_CA |
dc.identifier.issn | 0378-4754 | |
dc.identifier.uri | http://hdl.handle.net/10234/190049 | |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 14 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Elsevier | ca_CA |
dc.relation.isPartOf | Mathematics and Computers in Simulation (2020). | ca_CA |
dc.rights | 0378-4754/© 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rightsreserved | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | Concentrated solar power | ca_CA |
dc.subject | Deep learning | ca_CA |
dc.subject | Neural networks | ca_CA |
dc.subject | Forecasting | ca_CA |
dc.title | Deep learning-based forecasting of aggregated CSP production | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1016/j.matcom.2020.02.007 | |
dc.relation.projectID | JI-B2017-26, ACIF/2019/106 | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.sciencedirect.com/science/article/pii/S037847542030046X | ca_CA |
dc.date.embargoEndDate | 2022-02-25 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
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