Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts

For operational planning it is important to provide information about the situation-dependent uncertainty of a wind power forecast. Factors which influence the uncertainty of a wind power forecast include the predictability of the actual meteorological situation, the level of the predicted wind speed (due to the non-linearity of the power curve) and the forecost horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables ore considered, some inspired by the literature. The article contains an overview of related work within the field. An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error con be used to build a model of the quantiles of the forecast error. Only explanatory variables or indices which are predictable are considered, whereby the model obtained can be used for providing situation-dependent information regarding the uncertainty. Finally, the article contains directions enabling the reader to replicate the methods and thereby extend other forecast systems with situation-dependent information on uncertainty. Copyright (c) 2005 John Wiley & Sons, Ltd.