Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47656
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dc.contributor.authorDavid, Mathieuen_US
dc.contributor.authorLuis, Mazorra Aguiaren_US
dc.contributor.authorLauret, Philippeen_US
dc.date.accessioned2018-11-23T15:19:36Z-
dc.date.available2018-11-23T15:19:36Z-
dc.date.issued2018en_US
dc.identifier.issn0169-2070en_US
dc.identifier.urihttp://hdl.handle.net/10553/47656-
dc.description.abstractAccurate solar forecasts are necessary to improve the integration of solar renewables into the energy grid. In recent years, numerous methods have been developed for predicting the solar irradiance or the output of solar renewables. By definition, a forecast is uncertain. Thus, the models developed predict the mean and the associated uncertainty. Comparisons are therefore necessary and useful for assessing the skill and accuracy of these new methods in the field of solar energy. The aim of this paper is to present a comparison of various models that provide probabilistic forecasts of the solar irradiance within a very strict framework. Indeed, we consider focusing on intraday forecasts, with lead times ranging from 1 to 6 h. The models selected use only endogenous inputs for generating the forecasts. In other words, the only inputs of the models are the past solar irradiance data. In this context, the most common way of generating the forecasts is to combine point forecasting methods with probabilistic approaches in order to provide prediction intervals for the solar irradiance forecasts. For this task, we selected from the literature three point forecasting models (recursive autoregressive and moving average (ARMA), coupled autoregressive and dynamical system (CARDS), and neural network (NN)), and seven methods for assessing the distribution of their error (linear model in quantile regression (LMQR), weighted quantile regression (WQR), quantile regression neural network (QRNN), recursive generalized autoregressive conditional heteroskedasticity (GARCHrls), sieve bootstrap (SB), quantile regression forest (QRF), and gradient boosting decision trees (GBDT)), leading to a comparison of 20 combinations of models. None of the model combinations clearly outperform the others; nevertheless, some trends emerge from the comparison. First, the use of the clear sky index ensures the accuracy of the forecasts. This derived parameter permits time series to be deseasonalized with missing data, and is also a good explanatory variable of the distribution of the forecasting errors. Second, regardless of the point forecasting method used, linear models in quantile regression, weighted quantile regression and gradient boosting decision trees are able to forecast the prediction intervals accurately.en_US
dc.languageengen_US
dc.relationIntegración de Nuevas Metodologías en Simulación de Campos de Viento, Radiación Solar y Calidad Del Aireen_US
dc.relation.ispartofInternational Journal of Forecastingen_US
dc.sourceInternational Journal of Forecasting [ISSN 0169-2070], v. 34 (3), p. 529-547en_US
dc.subject1203 Ciencia de los ordenadoresen_US
dc.subject.otherPrediction intervalsen_US
dc.subject.otherProbabilistic forecastingen_US
dc.subject.otherSolar forecastingen_US
dc.subject.otherTime seriesen_US
dc.subject.otherVery short term horizonsen_US
dc.titleComparison of intraday probabilistic forecasting of solar irradiance using only endogenous dataen_US
dc.typeinfo:eu-repo/semantics/articlees
dc.typeArticlees
dc.identifier.doi10.1016/j.ijforecast.2018.02.003en_US
dc.identifier.scopus85046850329-
dc.identifier.isi000449722000010-
dc.contributor.authorscopusid35486904800-
dc.contributor.authorscopusid56971482900-
dc.contributor.authorscopusid7004327525-
dc.identifier.eissn1872-8200-
dc.description.lastpage547-
dc.identifier.issue3-
dc.description.firstpage529-
dc.relation.volume34-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.identifier.ulpgces
dc.description.sjr1,535
dc.description.jcr3,386
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.ssciSSCI
dc.description.erihplusERIH PLUS
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.project.principalinvestigatorEscobar Sánchez, José M-
crisitem.author.deptGIR SIANI: Modelización y Simulación Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Ingeniería Eléctrica-
crisitem.author.orcid0000-0002-9746-7461-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameMazorra Aguiar, Luis-
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