Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types

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dc.contributor.author Galván, Inés M.
dc.contributor.author Huertas Tato, Javier
dc.contributor.author Rodríguez Benítez, Francisco J.
dc.contributor.author Arbizu Barrena, Clara
dc.contributor.author Pozo Vázquez, David
dc.contributor.author Aler, Ricardo
dc.date.accessioned 2022-01-21T09:36:50Z
dc.date.available 2022-01-21T09:36:50Z
dc.date.issued 2021-09-01
dc.identifier.bibliographicCitation Galván, I., Huertas, J., Rodríguez, F., Arbizu, C., Pozo, D., Aler, R. (2021). Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types. Applied Soft Computing, 109, 107531.
dc.identifier.issn 1568-4946
dc.identifier.uri http://hdl.handle.net/10016/33930
dc.description.abstract Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration of four Global Horizontal Irradiance (GHI) forecasting models (Smart Persistence, WRF-solar, CIADcast, and Satellite) is addressed. Several short-term forecasting horizons, up to one hour ahead, have been analyzed. Within this context, one of the aims of the article is to study whether knowledge about the synoptic weather conditions, which are related to the stability of weather, might help to reduce the uncertainty represented by prediction intervals. In order to deal with this issue, information about which weather type is present at the time of prediction, has been used by the blending model. Four weather types have been considered. A multi-objective variant of the Lower Upper Bound Estimation approach has been used in this work for prediction interval estimation and compared with two baseline methods: Quantile Regression (QR) and Gradient Boosting (GBR). An exhaustive experimental validation has been carried out, using data registered at Seville in the Southern Iberian Peninsula. Results show that, in general, using weather type information reduces uncertainty of prediction intervals, according to all performance metrics used. More specifically, and with respect to one of the metrics (the ratio between interval coverage and width), for high-coverage (0.90, 0.95) prediction intervals, using weather type enhances the ratio of the multi-objective approach by 2%¿. Also, comparing the multi-objective approach versus the two baselines for high-coverage intervals, the improvement is 11%¿% over QR and 10%¿% over GBR. Improvements for low-coverage intervals (0.85) are smaller.
dc.description.sponsorship The authors are supported by projects funded by Agencia Estatal de Investigación, Spain (PID2019-107455RB-C21 and PID2019-107455RB-C22/AEI/10.13039/501100011033). Also supported by Spanish Ministry of Economy and Competitiveness, project ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The University of Jaén team is also supported by FEDER, Spain funds and by the Junta de Andalucía, Spain (Research group TEP-220)
dc.format.extent 13
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2021 The Authors. Published by Elsevier B.V.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other blending approaches
dc.subject.other multi-objective optimization
dc.subject.other prediction intervals
dc.subject.other solar forecasting
dc.title Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1016/j.asoc.2021.107531
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-2-R
dc.relation.projectID Gobierno de España. ENE2014-56126-C2-1-R
dc.relation.projectID Gobierno de España. PID2019-107455RB-C21
dc.relation.projectID Gobierno de España. PID2019-107455RB-C22
dc.type.version publishedVersion
dc.identifier.publicationtitle APPLIED SOFT COMPUTING
dc.identifier.publicationvolume 109
dc.identifier.uxxi AR/0000028899
dc.contributor.funder Agencia Estatal de Investigación (España)
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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