Deep neural networks for the quantile estimation of regional renewable energy production

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dc.contributor.author Alcántara Mata, Antonio
dc.contributor.author Galván, Inés M.
dc.contributor.author Aler, Ricardo
dc.date.accessioned 2022-09-13T09:02:09Z
dc.date.available 2022-09-13T09:02:09Z
dc.date.issued 2022-08-02
dc.identifier.bibliographicCitation Alcántara, A., Galván, I.M. & Aler, R. Deep neural networks for the quantile estimation of regional renewable energy production. Appl Intell (2022). https://doi.org/10.1007/s10489-022-03958-7
dc.identifier.issn 0924-669X
dc.identifier.uri http://hdl.handle.net/10016/35678
dc.description.abstract Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage.
dc.format.extent 36
dc.language.iso eng
dc.publisher Springer
dc.rights © The Author(s) 2022
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other deep neural networks
dc.subject.other prediction intervals
dc.subject.other probabilistic forescasting
dc.subject.other quantile estimation
dc.subject.other regional renewable energy forescasting
dc.title Deep neural networks for the quantile estimation of regional renewable energy production
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1007/s10489-022-03958-7
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. PID2019-107455RB-C22
dc.type.version publishedVersion
dc.identifier.publicationtitle APPLIED INTELLIGENCE
dc.identifier.uxxi AR/0000031059
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
dc.contributor.funder Agencia Estatal de Investigación (España)
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