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The document proposes a data mining approach combining K-means clustering and bagging neural networks for short-term wind power forecasting. K-means clustering is used to classify historical data into categories based on similarity of weather conditions. A bagging-based ensemble approach is integrated with backpropagation neural networks to overcome overfitting and instability issues. The proposed approach is evaluated using real wind generation data and shown to improve forecasting accuracy compared to oth

Author

Romeu Bertho

Language

EN