A combination of supervised dimensionality reduction and learning methods to forecast solar radiation

García Cuesta, Esteban; Aler, Ricardo; Pozo Vázquez, David; Galván, Inés M.

Publication:
A combination of supervised dimensionality reduction and learning methods to forecast solar radiation

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)	es
dc.contributor.author	García Cuesta, Esteban
dc.contributor.author	Aler, Ricardo
dc.contributor.author	Pozo Vázquez, David
dc.contributor.author	Galván, Inés M.
dc.contributor.funder	Comunidad de Madrid	es
dc.contributor.funder	Agencia Estatal de Investigación (España)	es
dc.date.accessioned	2022-10-20T07:46:28Z
dc.date.available	2022-10-20T07:46:28Z
dc.date.issued	2022-10-06
dc.description.abstract	Machine learning is routinely used to forecast solar radiation from inputs, which are forecasts of meteorological variables provided by numerical weather prediction (NWP) models, on a spatially distributed grid. However, the number of features resulting from these grids is usually large, especially if several vertical levels are included. Principal Components Analysis (PCA) is one of the simplest and most widely-used methods to extract features and reduce dimensionality in renewable energy forecasting, although this method has some limitations. First, it performs a global linear analysis, and second it is an unsupervised method. Locality Preserving Projection (LPP) overcomes the locality problem, and recently the Linear Optimal Low-Rank (LOL) method has extended Linear Discriminant Analysis (LDA) to be applicable when the number of features is larger than the number of samples. Supervised Nonnegative Matrix Factorization (SNMF) also achieves this goal extending the Nonnegative Matrix Factorization (NMF) framework to integrate the logistic regression loss function. In this article we try to overcome all these issues together by proposing a Supervised Local Maximum Variance Preserving (SLMVP) method, a supervised non-linear method for feature extraction and dimensionality reduction. PCA, LPP, LOL, SNMF and SLMVP have been compared on Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) radiation data at two different Iberian locations: Seville and Lisbon. Results show that for both kinds of radiation (GHI and DNI) and the two locations, SLMVP produces smaller MAE errors than PCA, LPP, LOL, and SNMF, around 4.92% better for Seville and 3.12% for Lisbon. It has also been shown that, although SLMVP, PCA, and LPP benefit from using a non-linear regression method (Gradient Boosting in this work), this benefit is larger for PCA and LPP because SMLVP is able to perform non-linear transformations of inputs.	en
dc.description.sponsorship	This work has been made possible by projects funded by Agencia Estatal de Investigación (PID2019-107455RB-C22 / AEI / 10.13039/501100011033). This work was also supported by the Comunidad de Madrid Excellence Program and Comunidad de Madrid-Universidad Politécnica de Madrid young investigators initiative.	en
dc.identifier.bibliographicCitation	García-Cuesta, E., Aler, R., Pózo-Vázquez, D.d. et al. A combination of supervised dimensionality reduction and learning methods to forecast solar radiation. Appl Intell (2022). https://doi.org/10.1007/s10489-022-04175-y	en
dc.identifier.doi	https://doi.org/10.1007/s10489-022-04175-y
dc.identifier.issn	0924-669X
dc.identifier.publicationfirstpage	1
dc.identifier.publicationlastpage	14
dc.identifier.publicationtitle	APPLIED INTELLIGENCE	en
dc.identifier.uri	https://hdl.handle.net/10016/35905
dc.identifier.uxxi	AR/0000031294
dc.language.iso	eng	en
dc.publisher	Springer	en
dc.relation.projectID	Gobierno de España. PID2019-107455RB-C22	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	dimensionality reduction	en
dc.subject.other	hybrid learning	en
dc.subject.other	solar radiation forecast	en
dc.subject.other	data mining	en
dc.title	A combination of supervised dimensionality reduction and learning methods to forecast solar radiation	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication