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• W2189247654 abstract "A precise wind power prediction is important for the integration of wind energy into the power grid. Besides numerical weather models for short-term predictions, there is a trend towards the development of statistical data-driven models that can outperform the classical forecast models [1]. In this paper, we improve a statistical prediction model proposed by Kramer and Gieseke [5], by employing a cross-correlation weighted k-nearest neighbor regression model (x-kNN). We demonstrate its superior performance by the comparison with the standard u-kNN method. Even if different pre-processing steps are considered, our regression technique achieves a comparably high accuracy." @default.
• W2189247654 created "2016-06-24" @default.
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• W2189247654 date "2014-01-01" @default.
• W2189247654 modified "2023-09-24" @default.
• W2189247654 title "Wind Power Prediction with Cross-Correlation Weighted Nearest Neighbors" @default.
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