Short-Term Wind Power Prediction Method Based on Multi-feature Adaptive Fusion and Extreme Value Theory Tail Calibration

Abstract

Accurate short-term wind power prediction is a critical foundation for improving the accommodation capacity and dispatching security of power systems. Aiming at the problems of multicollinearity in traditional models and large prediction errors under extreme fluctuations, this paper proposes a prediction method combining multi-feature fusion ridge regression with extreme value theory. First, multiple ridge regression branches are constructed, and multi-dimensional electrical features such as voltage and reactive power are introduced. L2 regularization is applied to alleviate variable multicollinearity, and a difference strategy is adopted to adapt to the inertial characteristics of short-term power. Second, the optimal backbone model for different prediction horizons is adaptively matched based on validation set errors, and weighted fusion with the persistence model is performed to output point prediction results. Finally, the POT-GPD model is used to fit the tail of errors, and a 95% probabilistic prediction interval is constructed. Experiments show that the average error of the proposed model across multiple horizons outperforms comparison models, and the prediction interval coverage rate exceeds 95%, which balances both conventional prediction accuracy and early warning capability for extreme wind power fluctuation risks.