Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals

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dc.contributor.author Martin Vazquez, Ruben
dc.contributor.author Huertas Tato, Javier
dc.contributor.author Aler, Ricardo
dc.contributor.author Galván, Inés M.
dc.date.accessioned 2019-10-10T08:16:35Z
dc.date.available 2019-10-10T08:16:35Z
dc.date.issued 2018-11-09
dc.identifier.bibliographicCitation Martín-Vázquez, R., Huertas-Tato, J., Aler, R., Galván, I.M. (2018) Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals. In Intelligent Data Engineering and Automated Learning, pp. 155-162, (11315).
dc.identifier.isbn 978-3-030-03495-5
dc.identifier.uri http://hdl.handle.net/10016/29003
dc.description This paper has been presented at: 19th Intelligent Data Engineering and Automated Learning (IDEAL 2018)
dc.description.abstract While it is common to make point forecasts for solar energy generation, estimating the forecast uncertainty has received less attention. In this article, prediction intervals are computed within a multi-objective approach in order to obtain an optimal coverage/width tradeoff. In particular, it is studied whether using measured power as an another input, additionally to the meteorological forecast variables, is able to improve the properties of prediction intervals for short time horizons (up to three hours). Results show that they tend to be narrower (i.e. less uncertain), and the ratio between coverage and width is larger. The method has shown to obtain intervals with better properties than baseline Quantile Regression.
dc.description.sponsorship This work has been funded by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL project).
dc.format.extent 8
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer Nature Switzerland AG 2018.
dc.subject.other Solar energy
dc.subject.other Prediction intervals
dc.subject.other Multi-objective optimization
dc.title Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1007/978-3-030-03496-2_18
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-2-R
dc.type.version acceptedVersion
dc.relation.eventdate 21-23 November 2018
dc.relation.eventplace Madrid, Spain
dc.relation.eventtitle 19th International Conference on Intelligent Data Engineering and Automated Learning (2018)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 155
dc.identifier.publicationlastpage 162
dc.identifier.publicationtitle Intelligent Data Engineering and Automated Learning - IDEAL 2018
dc.identifier.publicationvolume 11315
dc.identifier.uxxi CC/0000030018
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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