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

Alcántara Mata, Antonio; Galván, Inés M.; Aler, Ricardo

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

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.dpto	UC3M. Departamento de Estadística	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)	es
dc.contributor.author	Alcántara Mata, Antonio
dc.contributor.author	Galván, Inés M.
dc.contributor.author	Aler, Ricardo
dc.contributor.funder	Comunidad de Madrid	es
dc.contributor.funder	Ministerio de Ciencia e Innovación (España)	es
dc.contributor.funder	Agencia Estatal de Investigación (España)	es
dc.date.accessioned	2022-09-13T09:02:09Z
dc.date.available	2022-09-13T09:02:09Z
dc.date.issued	2022-08-02
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.	en
dc.description.sponsorship	This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN /AEI / 10.13039 / 501100011033. This work was also supported by the Comunidad de Madrid Excellence Program.	en
dc.description.sponsorship	Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.	en
dc.format.extent	36
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	en
dc.identifier.doi	https://doi.org/10.1007/s10489-022-03958-7
dc.identifier.issn	0924-669X
dc.identifier.publicationtitle	APPLIED INTELLIGENCE	en
dc.identifier.uri	https://hdl.handle.net/10016/35678
dc.identifier.uxxi	AR/0000031059
dc.language.iso	eng	en
dc.publisher	Springer	en
dc.relation.projectID	Gobierno de España. PID2019-107455RB-C22	es
dc.relation.projectID	AT-2022	es
dc.rights	© The Author(s) 2022	en
dc.rights	Atribución 3.0 España
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/
dc.subject.eciencia	Informática	es
dc.subject.other	deep neural networks	en
dc.subject.other	prediction intervals	en
dc.subject.other	probabilistic forescasting	en
dc.subject.other	quantile estimation	en
dc.subject.other	regional renewable energy forescasting	en
dc.title	Deep neural networks for the quantile estimation of regional renewable energy production	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication