The integration of wind power into smart grids has become increasingly crucial for a sustainable energy future, but it relies heavily on the accuracy of daily forecasts to ensure efficient management and minimize reliance on fossil fuels. To address the limitations of current forecasting models, which often function as "black boxes" lacking transparency, researchers have turned to explainable artificial intelligence (XAI). By applying XAI techniques to wind power generation, engineers can enhance the interpretability of forecasts, providing insights into the decision-making processes of AI models and identifying key variables that contribute to reliable predictions.
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