A probabilistic alert system for extreme wind events prediction using quantile regression ensembles

Anticipating and mitigating the impact of extreme wind events is increasingly critical as wind power becomes a central component of modern energy systems. However, existing predictive approaches often struggle to capture the uncertainty and variability inherent in wind data, limiting their effectiveness in risk management. This research aims to develop a probabilistic alert system to predict the occurrence of such extreme events effectively. To achieve this, a novel framework is proposed, combining quantile regression and kernel density estimation, to construct a robust predictive ensemble system. By integrating individual quantile regression predictions across multiple quantiles, the proposed framework captures the inherent variability and uncertainty of wind data. Additionally, the ensemble model’s probabilistic outputs are calibrated using isotonic regression, yielding refined distributions that closely align with observed extreme event occurrence rates. The framework was validated using real-world data from a wind farm in Spain, showing substantial improvements over conventional probabilistic binary classifiers in both accuracy and calibration of extreme event probabilities. These findings highlight the potential of the proposed system to enhance operational decision-making and resilience in wind power infrastructure under extreme weather conditions.