Statistical post-processing of turbulence-resolving weather forecasts for offshore wind power forecasting

Accurate short-term power forecasts are crucial for the reliable and efficient integration of wind energy in power systems and electricity markets. Typically, forecasts for hours to days ahead are based on the output of numerical weather prediction models, and with the advance of computing power, the spatial and temporal resolutions of these models have increased substantially. However, high-resolution forecasts often exhibit spatial and/or temporal displacement errors, and when regarding typical average performance metrics, they often perform worse than smoother forecasts from lower-resolution models. Recent computational advances have enabled the use of large-eddy simulations (LESs) in the context of operational weather forecasting, yielding turbulence-resolving weather forecasts with a spatial resolution of 100 m or finer and a temporal resolution of 30 seconds or less. This paper is a proof-of-concept study on the prospect of leveraging these ultra high-resolution weather models for operational forecasting at Horns Rev I in Denmark. It is shown that temporal smoothing of the forecasts clearly improves their skill, even for the benchmark resolution forecast, although potentially valuable high-frequency information is lost. Therefore, a statistical post-processing approach is explored on the basis of smoothing and feature engineering from the high-frequency signal. The results indicate that for wind farm forecasting, using information content from both the standard and LES resolution models improves the forecast accuracy, especially with a feature selection stage, compared with using the information content solely from either source.