Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment

The paper presents an advanced wind forecasting system that uses on-line SCADA measurements, as well as numerical weather predictions (NWP) as input, to predict the power production of wind parks 48 hours ahead. The prediction system integrates models based on adaptive fuzzy-neural networks configured either for short-term (1-10 hours) or long-term (1-48 hours) forecasting. The paper presents detailed one-year evaluation results of the models on the case study of Ireland, where the output of several wind farms is predicted using HIRLAM meteorological forecasts as input. A method for the online estimation of confidence intervals of the forecasts is developed together with an appropriate index for assessing online the risk due to the inaccuracy of the numerical weather predictions.