Very short-term probabilistic wind power forecasting based on Markov chain models

Abstract

Wind power forecasting methods generally provide estimates of future wind power as point forecasts, but most of the decision making processes in electrical power systems management require more information than a single value. For this purpose, additional methods - complex or based on strong assumptions - have been developed for estimating so-called interval forecasts associated to point forecasts. The method proposed by the authors is based on the use of discrete time Markov chain models of a proper order, developed starting from wind power time series analysis. It allows to directly obtain in an easy way an estimate of the wind power distributions on a very short-term horizon, without requiring restrictive assumptions on wind power probability distribution. With reference to an application, results obtained via a First and Second Order Markov Chain Model, respectively, are compared to those of Persistent Model evaluating the related prediction errors.