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The paper introduces a new methodology for assessing on-line the predictionrisk of short-term wind power forecasts. The first part of this methodologyconsists in computing confidence intervals with a confidence level defined bythe end-user. The resampling approach is used for this purpose since it permitsto avoid a restrictive hypothesis on the distribution of the errors. It hasbeen however appropriately adapted for the wind power prediction problem takinginto account the dependency of the errors on the level of predicted powerthrough appropriately defined fuzzy sets. The second part of the proposedmethodology introduces two indices, named as MRI and PRI, that quantify themeteorological risk by measuring the spread of multi-scenario Numerical WeatherPredictions and wind power predictions respectively. The multi-scenarioforecasts considered here are based on the 'poor man's' ensembles approach. Thetwo indices are used either to fine-tune the confidence intervals or to givesignals to the operator on the prediction risk, i.e. the probabilities for theoccurrence of high prediction errors depending on the weather stability. Arelation between these indices and the level of prediction error is shown.Evaluation results over a three-year period on the case of a wind farm inDenmark and over a one-year period on the case of several farms in Ireland aregiven. The proposed methodology has an operational nature and can be applied toall kinds of wind power forecasting models

Uncertainty of short-term Wind Power Forecasts -- A methodology for on-line Assessment