Pinson, Pierre3; Møller, Jan Kloppenborg4; Nielsen, Henrik Aalborg, orlov 31.07.20083; Madsen, Henrik4; Kariniotakis, George N.5
1 Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 Department of Electrical Engineering, Technical University of Denmark4 Department of Applied Mathematics and Computer Science, Technical University of Denmark5 unknown
Predictions of wind power production for horizons up to 48-72 hour ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the most likely outcome for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from nonparametric methods, and then take the form of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind power. These distributions are defined by 18 quantile forecasts with nominal proportions spanning the unit interval. The relevance and interest of the introduced evaluation framework are consequently discussed.