  The importance of renewable power production is a set goal in terms of the
energy turnaround. Developing short-term wind speed forecasting improvements
might increase the profitability of wind power. This article compares two novel
approaches to model and predict wind speed. Both approaches incorporate
periodic interactions, whereas the first model uses Fourier series to model the
periodicity. The second model takes $l_{2,p}$ generalised trigonometric
functions into consideration. The aforementioned Fourier series are special
types of the p-generalised trigonometrical function and therefore model 1 is
nested in model 2. The two models use an ARFIMA-APARCH process to cover the
autocorrelation and the heteroscedasticity. A data set which consist of 10
minute data collected at four stations at the German-Polish border from August
2007 to December 2012 is analysed. The most important finding is an enhancement
of the forecasting accuracy up to three hours that is directly related to our
new short-term forecasting model.
