Vorhersagen der Windgeschwindigkeit und Windenergie in Deutschland

The importance of wind power as well as wind speed predictions increases with energy transition in Germany. Accurate short and medium term wind speed and wind energy predictions are essential in different fields of economy. Implementation and application of new wind forecasting models helps to increase the benefit of wind power. This paper deals with applications of the periodic seasonal vector autoregressive prediction model (VAR). Moreover, the threshold autoregressive conditional heteroscedasticity (TARCH) is considered. The introduced model is estimated using iteratively reweighted least absolute shrinkage and selection (LASSO). This method is compared to ordinary least squares (OLS) estimation, a pure LASSO approach as well as several benchmark models. In addition, wind energy predictions are computed. Moreover, both the in-sample performance and their out-of-sample accuracy are discussed. The findings are summarized in an overview of different time series wind speed prediction models and their accuracy. We provide the forecasting performance up to 48 h. Finally, we cover the problem of transforming wind speed to wind energy. Copyright Springer-Verlag Berlin Heidelberg 2016