Obtaining superior wind power predictions from a periodic and heteroscedastic Wind Power Prediction Tool

The Wind Power Prediction Tool (WPPT) has successfully been used for accurate wind power forecasts in the short to medium term scenario (up to 12 hours ahead). Since its development about a decade ago, a lot of additional stochastic modeling has been applied to the interdependency of wind power and wind speed. We improve the model in three ways: First, we replace the rather simple Fourier series of the basic model by more general and flexible periodic Basis splines (Bsplines). Second, we model conditional heteroscedasticity by a threshold-GARCH (TGARCH) model, one aspect that is entirely left out by the underlying model. Third, we evaluate several distributional forms of the model's error term. While the original WPPT assumes gaussian errors only, we also investigate whether the errors may follow a Student's t-distribution as well as a skew t-distribution. In this article we show that our periodic WPPT-CH model is able to improve forecasts' accuracy significantly, when compared to the plain WPPT model.