https://hal-mines-paristech.archives-ouvertes.fr/hal-01721503Pinson, PierrePierrePinsonCEP/Sophia - CEP - Centre Énergétique et Procédés - MINES ParisTech - École nationale supérieure des mines de Paris - PSL - Université Paris sciences et lettresKariniotakis, GeorgesGeorgesKariniotakisCEP/Sophia - CEP - Centre Énergétique et Procédés - MINES ParisTech - École nationale supérieure des mines de Paris - PSL - Université Paris sciences et lettresOn-line Assessment of Prediction Risk for Wind Power Production ForecastsHAL CCSD2004Wind powershort-term forecastingconfidence intervalsprediction riskon-line softwareadaptive fuzzy-neural networksnumerical weather predictions[SPI.ENERG] Engineering Sciences [physics]/EnergeticsPrudon, Magalie2018-03-02 11:11:252021-11-17 12:33:042018-03-02 16:03:35enJournal articleshttps://hal-mines-paristech.archives-ouvertes.fr/hal-01721503/document10.1002/we.114application/pdf1The article introduces a new methodology for assessing on-line the prediction risk of short-term wind power forecasts. The first stage of this methodology consists in computing confidence intervals with a confidence level defined by the end-user. The resampling approach is used, which, in contrast to existing methods for wind forecasting, does not need to make a restrictive hypothesis on the distribution of the errors. To account for the non-linearity of the power curve and the cut-off effect, the errors are classified using appropriate fuzzy sets. The confidence intervals are then fine-tuned to reduce their width in the case of stable weather conditions. For this purpose an appropriate index, called the ‘meteo-risk index’ (MRI), is defined reflecting the spread of the available numerical weather predictions. A linear relation between that index and the resulting prediction error is shown. The second part of the methodology is to use the MRI itself as a preventive on-line tool to derive signals for the operator on the meteorological risk, i.e. the probabilities of the occurrence of high prediction errors depending on the weather stability. Evaluation results of this methodology over a 1 year period on the case study of Ireland are given, where the output of several wind farms is predicted using a dynamic fuzzy neural network-based model. The proposed methodology is generic and can be applied to all kinds of wind power prediction models.