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Wind Power Prediction with Cross-Correlation Weighted Nearest Neighbors
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Text/Conference Paper
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Datum
2014
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BIS-Verlag
Zusammenfassung
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.