During the last few years, probabilistic wind power forecasts have received increasing attention because of their assumed value in decision-making processes. In the current article, three statistical methods are described and several models based on these are compared. The statistical methods are local quantile regression, a local Gaussian model and the Nadaraya–Watson estimator for conditional cumulative distribution functions. The focus is on quantile forecasts, since these often provide the required type of information to make optimal economic decisions and are ideal for visualizing uncertainty. The statistical methods are applied to data from a wind farm in Norway and results are compared using appropriate measures for assessment of quantile forecasts and in terms of a simple model for economic value. Copyright © 2005 John Wiley & Sons, Ltd.