  In the last decades wind power became the second largest energy source in the
EU covering 16% of its electricity demand. However, due to its volatility,
accurate short range wind power predictions are required for successful
integration of wind energy into the electrical grid. Accurate predictions of
wind power require accurate hub height wind speed forecasts, where the state of
the art method is the probabilistic approach based on ensemble forecasts
obtained from multiple runs of numerical weather prediction models.
Nonetheless, ensemble forecasts are often uncalibrated and might also be
biased, thus require some form of post-processing to improve their predictive
performance. We propose a novel flexible machine learning approach for
calibrating wind speed ensemble forecasts, which results in a truncated normal
predictive distribution. In a case study based on 100m wind speed forecasts
produced by the operational ensemble prediction system of the Hungarian
Meteorological Service, the forecast skill of this method is compared with the
predictive performance of three different ensemble model output statistics
approaches and the raw ensemble forecasts. We show that compared with the raw
ensemble, post-processing always improves the calibration of probabilistic and
accuracy of point forecasts and from the four competing methods the novel
machine learning based approach results in the best overall performance.
