Probabilistic wind power forecasts using local quantile regression

Abstract Wind power forecasts are in various ways valuable for users in decision‐making processes. However, most forecasts are deterministic, and hence possibly important information about uncertainty is not available. Complete information about future production can be obtained by using probabilistic forecasts, and this article demonstrates how such forecasts can be created by means of local quantile regression. The approach has several advantages, such as no distributional assumptions and flexible inclusion of predictive information. In addition, it can be shown that, for some purposes, forecasts in terms of quantiles provide the type of information required to make optimal economic decisions. The methodology is applied to data from a wind farm in Norway. Copyright © 2004 John Wiley & Sons, Ltd.