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isabelbenz
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One-day ahead Power Forecasting is more and more required on the energy markets, and its accuracy is more and more crucial since it affects the net income of operators. 1. Weather Numerical Prediction, including a meso scale downscaling, provides a global prediction. A RANS CFD-tools is used for the micro-scale downscaling, providing a precise wind forecast at each wing generator hub. 2. To improve the reliability of this forecast, especially in the short term range, the use of "fresh" SCADA data is performed. Attention is focused on the Active Power, but other signals such as temperature and local wind characteristics can be taken into account. 3. In order to erase systematic errors and bias from the downscaled NWP based forecast (1.), as well as to mix it with the persistent model (2.), an Artificial Neural Network is trained using long term history. This paper explains first the method used and the choices made, especially concerning the Machine Learning parameters. A second part presents some results on some real cases, with different time horizons.
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One-day ahead Power Forecasting is more and more required on the energy markets, and its accuracy is more and more crucial since it affects the net income of operators. 1. Weather Numerical Prediction, including a meso scale downscaling, provides a global prediction. A RANS CFD-tools is used for the micro-scale downscaling, providing a precise wind forecast at each wing generator hub. 2. To improve the reliability of this forecast, especially in the short term range, the use of "fresh" SCADA data is performed. Attention is focused on the Active Power, but other signals such as temperature and local wind characteristics can be taken into account. 3. In order to erase systematic errors and bias from the downscaled NWP based forecast (1.), as well as to mix it with the persistent model (2.), an Artificial Neural Network is trained using long term history. This paper explains first the method used and the choices made, especially concerning the Machine Learning parameters. A second part presents some results on some real cases, with different time horizons.
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