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An advanced On-line Wind Resource Prediction system for the optimal management of wind park

Abstract : The paper presents an advanced wind forecasting system that uses on-line SCADA measurements, as well as numerical weather predictions as input to predict the power production of wind parks 48 hours ahead. The prediction tool integrates models based on adaptive fuzzy-neural networks configured either for short-term or long-term forecasting. In each case, the model architecture is selected through non-linear optimization techniques. The forecasting system is integrated within the MORE-CARE EMS software developed in the frame of a European research project. Within this on-line platform, the forecasting module provides forecasts and confidence intervals for the wind farms in a power system, which can be directly used by economic dispatch and unit commitment functions. The platform can run also as a stand-alone application destined only for wind forecasting. Detailed results are presented on the performance of the developed models on a real wind farm using HIRLAM numerical weather predictions as input.
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Submitted on : Friday, May 4, 2018 - 10:28:34 AM
Last modification on : Wednesday, November 17, 2021 - 12:30:34 PM


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  • HAL Id : hal-00534195, version 1


Georges Kariniotakis, Didier Mayer. An advanced On-line Wind Resource Prediction system for the optimal management of wind park. Med Power 2002, Nov 2002, Athènes, Greece. ⟨hal-00534195⟩



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