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

Abstract

Evaluations are presented for the prediction of wind power ramping events in the Belgian Offshore Zone. Two models from the Royal Meteorological Institute of Belgium are verified: the operational ALARO-4km and its version with Wind Farm Parameterization (WFP). Power predictions are produced using power curves and machine learning (ML). As standard metrics such as MAE are insufficient for evaluating ramps, the proposed framework incorporates time and power buffers, enabling a flexible assessment that tolerates minor errors. Results indicate that WFP models enhance ramping prediction skill, while ML provides more balanced forecasts by reducing both misses and false alarms. A Ramp Alignment Score is also introduced to quantify temporal errors by forecast lead time, confirming that WFP models yield smaller average timing errors. Moreover, the framework reveals that severe precipitation is a strong indicator of large, predictable ramps, whereas lighter precipitation is associated with greater forecast errors.