Improving the prediction of extreme wind speed events with generative data augmentation techniques

• Published in: Renewable energy 2024-02, Vol.221, p.119769, Article 119769
• Main Authors: Vega-Bayo, M., Pérez-Aracil, J., Prieto-Godino, L., Salcedo-Sanz, S.
• Format: Article
• Language: English
• Subjects: Data augmentation techniques; Extreme wind speed; Machine learning; Prediction systems; Variational autoencoders
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2023.119769

Extreme Wind Speed events (EWS) are responsible for the worst damages caused by wind in wind farms. An accurate estimation of the frequency and intensity of EWS is essential to avoid wind turbine damage and to minimize cut-out events in these facilities. In this paper we discuss how generative Data Augmentation (DA) techniques improve the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in EWS prediction problems. These problems are usually tackled as classification tasks, which are highly unbalanced due to the small number of EWS events in wind farms. Different versions of Variational AutoEncoders (VAE) are proposed and analysed in this work (VAEs, Conditional VAEs (CVAEs) and Class-Informed VAEs (CI-VAE)) as generative DA techniques to balance the data in EWS problems, leading to better performance of the prediction systems. The proposed generative DA techniques have been compared against traditional DA algorithms in a real problem of EWS prediction in Spain, considering ERA5 reanalysis data as predictive variables. The results showed that the CI-VAE with a Convolutional Neural Network approach obtained the best results, with values of Precision 0.62, Recall 0.74 and F1 score 0.67, improving up to 4% the results of the method without data augmentation techniques. [Display omitted] •Generative data augmentation techniques can improve extreme wind prediction.•Variational Autoencoders modifications are used to generate synthetic wind extreme samples.•Experiments on real wind speed data from a Spanish wind farm are presented.•Comparisons with traditional data augmentation techniques show prediction improvements.