A hierarchical classification/regression algorithm for improving extreme wind speed events prediction. (December 2022)

• Record Type: Journal Article
• Authors: Peláez-Rodríguez, C.; Pérez-Aracil, J.; Fister, D.; Prieto-Godino, L.; Deo, R.C.; Salcedo-Sanz, S.
• Is Part Of: Renewable energy. Volume 201(2022)Part 2
• Page Start: 157
• Page End: 178
• Publication Date: 2022-12
• Subjects: Wind speed extremes -- Wind extremes prediction -- Hierarchical classification/regression schemes -- Wind energy -- Machine learning; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.11.042
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: A novel method for prediction of the extreme wind speed events based on a Hierarchical Classification/Regression (HCR) approach is proposed. The idea is to improve the prediction skills of different Machine Learning approaches on extreme wind speed events, while preserving the prediction performance for steady events. The proposed HCR architecture rests on three distinctive levels: first, a data preprocessing level, where training data are divided into clusters and accordingly associated labels. At this point, balancing techniques are applied to increase the significance of clusters with poorly represented wind gusts data. At a second level of the architecture, the classification of each sample into the corresponding cluster is carried out. Finally, once we have determined the cluster a sample belongs to, the third level carries out the prediction of the wind speed value, by using the regression model associated with that particular cluster. The performance of the proposed HCR approach has been tested in a real database of hourly wind speed values in Spain, considering Reanalysis data as predictive variables. The results obtained have shown excellent prediction skill in the forecasting of extreme events, achieving a 96% extremes detection, while maintaining a reasonable performance in the non-extreme samples. The performance of the methods has also been assessed using forecast data (GFS) as predictors.