Data augmentation techniques for extreme wind prediction improvement
M. Vega-Bayo , A. Gómez-Orellana , V. Vargas , D. Guijo-Rubio , L. Cornejo-Bueno , J. Pérez-Aracil , S. Salcedo-Sanz
International work-conference on the interplay between natural and artificial computation, pp. 1-11, 2024Abstract
Predicting extreme winds (i.e. winds speed equal to or greater than 25 m/s), is essential to predict wind power and accomplish safe and efficient management of wind farms. Although feasible, predicting extreme wind with supervised classifiers and deep learning models is particularly difficult because of the low frequency of these events, which leads to highly unbalanced training datasets. To tackle this challenge, in this paper different traditional data augmentation techniques, such as random oversampling, SMOTE, time series data warping and multidimensional data warping, are used to generate synthetic samples of extreme wind and its predictors, such as previous samples of wind speed and meteorological variables of the surroundings. Results show that using data augmentation techniques with the right oversampling ratio leads to improvement in extreme wind prediction with most machine learning and deep learning models tested. In this paper, advanced data augmentation techniques, such as Variational Autoencoders (VAE), are also applied and evaluated when inputs are time series.
Cite this publication
BibTex
@inproceedings{vega-bayo2024data,
author = {Marta Vega-Bayo and Antonio Manuel Gómez-Orellana and Víctor Manuel Vargas and David Guijo-Rubio and Laura Cornejo-Bueno and Jorge Pérez-Aracil and Sancho Salcedo-Sanz},
title = {Data augmentation techniques for extreme wind prediction improvement},
booktitle = {International work-conference on the interplay between natural and artificial computation},
year = {2024},
pages = {1--11},
doi = {10.1007/978-3-031-61137-7_28}
}
APA
Vega-Bayo, M., Gómez-Orellana, A., Vargas, V., Guijo-Rubio, D., Cornejo-Bueno, L., Pérez-Aracil, J., Salcedo-Sanz, S. (2024). Data augmentation techniques for extreme wind prediction improvement. In International work-conference on the interplay between natural and artificial computation (pp. 1-11).
CV
M. Vega-Bayo (CA), A.M. Gómez-Orellana (CA), V.M. Vargas (CA), D. Guijo-Rubio (CA), L. Cornejo-Bueno (CA), J. Pérez-Aracil (CA), S. Salcedo-Sanz (CA), (3/7) "Data augmentation techniques for extreme wind prediction improvement". International work-conference on the interplay between natural and artificial computation, pp. 1-11, 2024.
RIS
TY - CONF T1 - Data augmentation techniques for extreme wind prediction improvement T2 - International work-conference on the interplay between natural and artificial computation AU - Vega-Bayo, Marta AU - Gómez-Orellana, Antonio Manuel AU - Vargas, Víctor Manuel AU - Guijo-Rubio, David AU - Cornejo-Bueno, Laura AU - Pérez-Aracil, Jorge AU - Salcedo-Sanz, Sancho JO - International work-conference on the interplay between natural and artificial computation JA - International work-conference on the interplay between natural and artificial computation Y1 - 2024 PY - 2024 SP - 1 EP - 11 DO - 10.1007/978-3-031-61137-7_28 ER -