Predicting wind power with reforecasts

Energy traders and decision-makers need accurate wind power forecasts. For this purpose, numerical weather predictions (NWPs) are often statistically postprocessed to correct systematic errors. This requires a dataset of past forecasts and observations that is often limited by frequent NWP model enhancements that change the statistical model properties. Reforecasts that recompute past forecasts with a recent model pro- vide considerably longer datasets but usually have weaker setups than operationalmodels. This study tests the reforecasts from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre forMedium-RangeWeather Forecasts (ECMWF) for wind power predictions. The NOAAreforecast clearly performs worse than the ECMWF reforecast, the operational ECMWF deterministic and ensemble forecasts, and a limited-area model of the Austrian weather service [Zentralanstalt fürMeteorologie und Geodynamik (ZAMG)]. On the contrary, the ECMWF reforecast has, of all testedmodels, the smallest squared errors and one of the highest financial values in an energy market.