Examinando por Autor "Casanova-Mateo, Carlos"

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Machine learning regression and classification methods for fog events prediction
(Elsevier, 2022) Castillo-Botón, Carlos; Casillas-Pérez, David; Casanova-Mateo, Carlos; Ghimire, Sujan; Cerro-Prada, Elena; Gutierrez, P.A.; Deo, Ravinesh; Salcedo-Sanz, Sancho
Atmospheric low-visibility events are usually associated with fog formation. Extreme low-visibility events deeply affect the air and ground transportation, airports and motor-road facilities causing accidents and traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many fog formation and lowvisibility prediction problems. The associated problem can be formulated either as a regression or as a classification task, which has an impact on the type of ML approach to be used and on the quality of the predictions obtained. In this paper we carry out a complete analysis of low-visibility events prediction problems, formulated as both regression and classification problems. We discuss the performance of a large number of ML approaches in each type of problem, and evaluate their performance under a common comparison framework. According to the obtained results, we will provide indications on what the most efficient formulation is to tackle low-visibility predictions and the best performing ML approaches for low-visibility events prediction.
Spatio-temporal analysis of wind resource in the Iberian Peninsula with data-coupled clustering
(Elsevier, 2018-01) Chidean, Mihaela I.; Caamaño, Antonio J; Ramiro-Bargueño, Julio; Casanova-Mateo, Carlos; Salcedo-Sanz, Sancho
In this paper a spatio-temporal analysis of wind power resource in the Iberian Peninsula is presented. The study uses the Second-Order Data-Coupled Clustering (SODCC) algorithm over reanalysis data in the for the period 1979 – 2014. Several characteristics of the method are detailed, such as the data-coupled clustering approach of SODCC, that ensures the non-singularity of the signal subspace within each cluster. The performance of the proposed approach and specific results obtained have been discussed in a case study in the Iberian Peninsula. In these results it is possible to identify different spatio-temporal patterns of the wind data statistics depending on the initialization year. Moreover, this work also shows that there is a close relationship between these spatio-temporal patterns with the wind energy production of the area under study, so the proposed analysis can be extended to wind farms efficiency production at the time scales considered.