Matthias Lange

TL;DR: In this paper, three measures are taken as best practices to reduce prediction errors: Combinations of different models can be done with power output forecast models as well as with numerical weather prediction models (multimodel and multischeme approaches).

TL;DR: In this article, the uncertainty of wind power predictions is investigated with a special focus on the important role of the nonlinear power curve, and the overall prediction accuracy is assessed and it is shown that due to the power curve the relative forecast error increases by a factor of 1.8-2.6 compared to the wind speed forecast.

TL;DR: In this paper, the authors assess the prediction accuracy of wind power prediction systems and compare the forecast error to meteorological situations, showing that wind speed dependent prediction error is correlated with meteorological conditions.

TL;DR: In this paper, an analytical model based on the spatial correlation function of the prediction error is derived to describe the statistical characteristics of arbitrary configurations of wind farms, and it is shown that the magnitude of the error reduction depends only weakly on the number of sites and is mainly determined by the size of the region.

TL;DR: In this article, the authors present results of the first ever intercomparison of a number of advanced prediction systems performed in the frame of the European project Anemos, and a framework for error characterization is developed consisting by a measure-and a distribution-oriented approach.