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Separating aleatoric and epistemic uncertainty in wind power forecasts reveals theoretically consistent responses to noise, distribution shift, and data scale, even without ground truth uncertainty labels.
Quantifying uncertainty in XAI explanations lets you choose interpretations with a desired confidence level, making AI more reliable in high-stakes power grid applications.