A three-stage method combining physical rules, RANSAC regression with IQR thresholds, and mathematical morphology identifies scattered and densely clustered outliers in wind speed-power data better than several baselines.
Wind power forecasting based on a spatialtemporal graph convolution network with limited engineering knowledge,
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Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology
A three-stage method combining physical rules, RANSAC regression with IQR thresholds, and mathematical morphology identifies scattered and densely clustered outliers in wind speed-power data better than several baselines.