Wind Power Forecast Uncertainty Using Dynamic Combination of Predictions

Abstract

The system operators rely on forecasting tools to promote security of supply in the case of contingent renewable generation upheaval, thus decreasing the chance of counter trading in the intraday markets. This work introduces a self-adaptive ensemble based method providing optimal point predictions under the square loss function constrained over the probability simplex. The output is used to centre a new nonparametric probabilistic power forecast that leverages linear interpolation of the order statistics, thus providing forecast uncertainty estimations. The proposed methodology shows competitive reliability, with coverage and sharpness characteristics that compare favourably with reference methods, thus enabling the perusal of forecast uncertainty in operations planning.