Publication:
Using a multi-view convolutional neural network to monitor solar irradiance

dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorHuertas Tato, Javier
dc.contributor.authorGalván, Inés M.
dc.contributor.authorAler, Ricardo
dc.contributor.authorRodríguez Benítez, Francisco J.
dc.contributor.authorPozo Vázquez, David
dc.contributor.funderEuropean Commissionen
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.date.accessioned2022-02-16T11:09:31Z
dc.date.available2022-05-01T23:00:06Z
dc.date.issued2021-04-21
dc.description.abstractIn the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model.en
dc.description.sponsorshipThis work has been made possible by the Ministerio de Economia y Empresa of Spain, under the project PROSOL (ENE2014-56126-C2). Authors from the University of Jaen are supported by the Junta de Andalucía (Research group TEP-220) and by FEDER funds. This work has been made possible by projects funded by Agencia Estatal de Investigación (PID2019-107455RB-C21 and PID2019-107455RB-C22 / AEI / 10.13039/501100011033).en
dc.identifier.bibliographicCitationHuertas-Tato, J., Galván, I.M., Aler, R. et al. Using a Multi-view Convolutional Neural Network to monitor solar irradiance. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-05959-yen
dc.identifier.doihttps://doi.org/10.1007/s00521-021-05959-y
dc.identifier.issn0941-0643
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage13
dc.identifier.publicationtitleNEURAL COMPUTING & APPLICATIONSen
dc.identifier.urihttps://hdl.handle.net/10016/34139
dc.identifier.uxxiAR/0000028958
dc.language.isoengen
dc.publisherSpringeren
dc.relation.projectIDGobierno de España. ENE2014-56126-C2-2-Res
dc.relation.projectIDGobierno de España. PID2019-107455RB-C22es
dc.rights© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Natureen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherdeep learningen
dc.subject.othermulti-view imageen
dc.subject.othersolar irradianceen
dc.titleUsing a multi-view convolutional neural network to monitor solar irradianceen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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