Predicting Global Irradiance Combining Forecasting Models Through Machine Learning

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dc.contributor.author Huertas Tato, Javier
dc.contributor.author Aler, Ricardo
dc.contributor.author Rodríguez Benítez, F. J.
dc.contributor.author Arbizu Barrena, C.
dc.contributor.author Galván, Inés M.
dc.date.accessioned 2019-10-10T09:26:32Z
dc.date.available 2019-10-10T09:26:32Z
dc.date.issued 2018-06-08
dc.identifier.bibliographicCitation Huertas-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.
dc.identifier.isbn 978-3-319-92638-4
dc.identifier.uri http://hdl.handle.net/10016/29004
dc.description This paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)
dc.description.abstract Predicting 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.
dc.description.sponsorship The 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).
dc.format.extent 12
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer International Publishing AG, part of Springer Nature 2018
dc.subject.other Global irradiance forecasting
dc.subject.other Machine learning
dc.subject.other Combining forecasting models
dc.title Predicting Global Irradiance Combining Forecasting Models Through Machine Learning
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1007/978-3-319-92639-1_52
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-1-R
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-2-R
dc.type.version acceptedVersion
dc.relation.eventdate 20-22 June 2018
dc.relation.eventplace Asturias, Oviedo, Spain
dc.relation.eventtitle 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 622
dc.identifier.publicationlastpage 633
dc.identifier.publicationtitle HAIS 2018: Hybrid Artificial Intelligent Systems
dc.identifier.publicationvolume 10870
dc.identifier.uxxi CC/0000030021
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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