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

作者：Ruoke Meng, Geert Smet, Dieter Van den Bleeken, Aaron Van Poecke, Hossein Tabari, Peter Hellinckx, Piet Termonia, Joris Van den Bergh · 发表于：Meteorological Applications · 年份：2026 · DOI：10.1002/met.70203 · 研究领域：Energy Load and Power Forecasting、Wind Energy Research and Development、Meteorological Phenomena and Simulations

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 alignme...