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Predicting Global Irradiance Combining Forecasting Models Through Machine Learning

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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.authorAler, Ricardo
dc.contributor.authorRodríguez Benítez, F. J.
dc.contributor.authorArbizu Barrena, C.
dc.contributor.authorGalván, Inés M.
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2019-10-10T09:26:32Z
dc.date.available2019-10-10T09:26:32Z
dc.date.issued2018-06-08
dc.descriptionThis paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)en
dc.description.abstractPredicting solar irradiance is an active research problem, with many physical models having being designed to accurately predict Global Horizontal Irradiance. However, some of the models are better at short time horizons, while others are more accurate for medium and long horizons. The aim of this research is to automatically combine the predictions of four different models (Smart Persistence, Satellite, Cloud Index Advection and Diffusion, and Solar Weather Research and Forecasting) by means of a state-of-the-art machine learning method (Extreme Gradient Boosting). With this purpose, the four models are used as inputs to the machine learning model, so that the output is an improved Global Irradiance forecast. A 2-year dataset of predictions and measures at one radiometric station in Seville has been gathered to validate the method proposed. Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons. Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.en
dc.description.sponsorshipThe authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R and FEDER funds. Some of the authors are also funded by the Junta de Andalucía (research group TEP-220).en
dc.format.extent12
dc.identifier.bibliographicCitationHuertas-Tato, J., Aler, R., Rodríguez-Benítez, F.J., Arbizu-Barrena, C., Pozo-Vázquez, D. y Galván, I.M. (2018). Predicting Global Irradiance Combining Forecasting Models Through Machine Learning. In HAIS 2018: Hybrid Artificial Intelligent Systems,10870, pp. 622-633.en
dc.identifier.doihttps://doi.org/10.1007/978-3-319-92639-1_52
dc.identifier.isbn978-3-319-92638-4
dc.identifier.publicationfirstpage622
dc.identifier.publicationlastpage633
dc.identifier.publicationtitleHAIS 2018: Hybrid Artificial Intelligent Systemsen
dc.identifier.publicationvolume10870
dc.identifier.urihttps://hdl.handle.net/10016/29004
dc.identifier.uxxiCC/0000030021
dc.language.isoengen
dc.publisherSpringeren
dc.relation.eventdate20-22 June 2018en
dc.relation.eventplaceAsturias, Oviedo, Spainen
dc.relation.eventtitle13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)en
dc.relation.projectIDGobierno de España. ENE2014-56126-C2-1-Res
dc.relation.projectIDGobierno de España. ENE2014-56126-C2-2-Res
dc.rights© Springer International Publishing AG, part of Springer Nature 2018en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherGlobal irradiance forecastingen
dc.subject.otherMachine learningen
dc.subject.otherCombining forecasting modelsen
dc.titlePredicting Global Irradiance Combining Forecasting Models Through Machine Learningen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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