A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance
Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs). These early warnings enable the timely initiation of more detailed system stability assessments and preventive actions. However, forecasting these events is challenging due to the inherent class imbalance in WPRE datasets, where ramp events are less frequent (typically less than 15\% of observed events) compared to normal conditions. Ignoring this characteristic undermines the performance of conventional machine learning models, which often ...
| Authors | Alejandro Morales-Hernández, Fabrizio De Caroa, Gian Marco Paldino et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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