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Conditional prediction intervals of wind power generation

Abstract : A generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform of the situation-specific uncertainty of point forecasts. In order to avoid a restrictive assumption on the shape of forecast error distributions, focus is given to an empirical and nonparametric approach named adapted resampling. This approach employs a fuzzy inference model that permits to integrate expertise on the characteristics of prediction errors for providing conditional interval forecasts. By simultaneously generating prediction intervals with various nominal coverage rates, one obtains full predictive distributions of wind generation. Adapted resampling is applied here to the case of an onshore Danish wind farm, for which three point forecasting methods are considered as input. The probabilistic forecasts generated are evaluated based on their reliability and sharpness, while compared to forecasts based on quantile regression and the climatology benchmark. The operational application of adapted resampling to the case of a large number of wind farms in Europe and Australia among others is finally discussed.
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Submitted on : Thursday, August 11, 2011 - 11:22:25 AM
Last modification on : Thursday, September 24, 2020 - 5:22:03 PM

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Pierre Pinson, Georges Kariniotakis. Conditional prediction intervals of wind power generation. IEEE Transactions on Power Systems, Institute of Electrical and Electronics Engineers, 2010, 25 (4), pp.Pages 1845-1856. ⟨10.1109/TPWRS.2010.2045774⟩. ⟨hal-00614425⟩

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