Hierarchical model-based imbalanced wind speed forecast correction

doi:10.3969/j.issn.1000-1565.2025.03.010

Abstract

Abstract: To address the data imbalance problem in wind speed forecast correction, a correction model based on a classification/regression hierarchical structure is proposed. The core idea of this model is to adopt a divide-and-conquer strategy to gradually address the data imbalance in wind speed data. In the classification layer, a reweighting strategy is used to preliminarily address the data imbalance. In the regression layer, a group-wise expansion training strategy is proposed to effectively correct samples incorrectly classified due to imbalance, further addressing the data imbalance issue. Additionally, a probability weighting method based on a greedy strategy is designed to provide more accurate predictions for confident samples. The corrected wind speed forecasts showed a 34.4% reduction in overall Mean Absolute Error, with a 69.0% reduction for extreme wind speed events, indicating that the model not only improves the prediction accuracy for extreme wind speed events but also maintains performance in predicting stable events.

Key words: wind speed forecast correction, hierarchical model, data imbalance, extreme wind speed prediction

CLC Number:

TP181

CAO Yang, ZHAI Junhai, HAN Ling. Hierarchical model-based imbalanced wind speed forecast correction[J]. Journal of Hebei University(Natural Science Edition), 2025, 45(3): 317-326.

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