Evaluating the Prediction of Wind Power Ramping Events in the Belgian Offshore Zone

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ABSTRACT This study provides a comprehensive evaluation for the prediction of wind power ramping events in the Belgian Offshore Zone. These rapid, large‐scale power fluctuations pose significant challenges to grid reliability. The research uses operational Numerical Weather Prediction (NWP) models from the Royal Meteorological Institute of Belgium, as well as its version enhanced with Wind Farm Parameterization (WFP). Power predictions are generated with both typical power curves and machine learning approaches. Standard verification metrics, such as Mean Absolute Error (MAE), often fail to capture the operational significance of ramp events. To address this, we develop a flexible verification framework designed to assess ramp forecast performance. This framework incorporates adjustable time and power buffers, which tolerate minor, operationally acceptable discrepancies in the timing and magnitude of predicted events. Application of this framework to both intraday and day‐ahead forecasts reveals that WFP‐enhanced models consistently improve ramp predictions over the operational baseline. Further analysis reveals that while the WFP model with power curves effectively reduced false alarms, it comes at the cost of more misses. In contrast, ML‐based approaches achieve slightly higher overall skill scores by striking a better balance between reducing these error types. Moreover, we introduce the Ramp Alignment Score (RAS), an event‐based metric that quantifies the temporal alignment between predicted and observed ramps, to supplement the model evaluation by lead time. RAS analysis demonstrates that WFP models achieve better temporal alignment and reveals a distinct diurnal cycle in ramping prediction errors. Finally, we investigate the impact of a specific meteorological driver, finding an association between severe precipitation and large, highly predictable ramp events. Conversely, moderate and light precipitation are linked to a higher incidence of missed events and false alarms. This work provides both an operationally relevant evaluation methodology and insights into ramp predictions under specific meteorological conditions.

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