Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms

Summary

This study demonstrates how assimilating offshore lidar and SCADA measurements into the WRF numerical weather prediction model via four-dimensional data assimilation (nudging) significantly improves wind speed and direction forecasting at Belgian offshore wind farms, particularly for turbines experiencing wake effects. Wind speed errors at downwind turbines were reduced from 10.5% to 5.2%, and directional errors decreased by a factor of 2.4, leading to more accurate hour-ahead power production predictions. The authors also characterise optimal nudging parameters for this offshore setting, with implications for wind resource assessment and future offshore measurement campaigns.

Regional applicability

This study focuses on the Belgian–Dutch North Sea offshore wind farm cluster and is directly applicable to United Kingdom offshore wind operations, particularly in the North Sea where similar atmospheric conditions and wind farm clustering occur. The methodology and insights on data assimilation practices for improving wind predictions in complex multi-farm settings are transferable to UK offshore wind energy management.

Key measures

Relative root-mean-square error for wind speed, wind direction error, power production outputs, sensitivity to nudging parameters

Outcomes reported

The study measured improvements in wind speed and direction predictions at offshore wind farms by assimilating lidar and SCADA observations into a weather model, and assessed resulting improvements in wind power production forecasts.

Topic tags