DocumentCode :
3059020
Title :
Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment
Author :
Pinson, P. ; Kariniotakis, G.N.
Author_Institution :
Center of Energy Studies, Ecole des Mines de Paris, Valbonne, France
Volume :
2
fYear :
2003
fDate :
23-26 June 2003
Abstract :
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.
Keywords :
SCADA systems; fuzzy neural nets; load forecasting; power system analysis computing; prediction theory; risk management; wind power plants; HIRLAM meteorological forecasts; Ireland; adaptive fuzzy-neural networks; numerical weather predictions; on-line SCADA measurements; risk assessment; wind farms; wind power forecasting; Adaptive systems; Fuzzy neural networks; Power measurement; Power system modeling; Predictive models; Production systems; Risk management; Weather forecasting; Wind energy; Wind forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Tech Conference Proceedings, 2003 IEEE Bologna
Print_ISBN :
0-7803-7967-5
Type :
conf
DOI :
10.1109/PTC.2003.1304289
Filename :
1304289
Link To Document :
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