基于层次模型的非平衡风速预报订正

doi:10.3969/j.issn.1000-1565.2025.03.010

摘要/Abstract

摘要： 针对风速预报订正中的数据非平衡问题,提出了一种基于分类/回归层次结构的订正模型.该模型的核心思想是采用分治策略,逐步解决风速数据中的非平衡问题.在分类层中,使用了重加权策略来初步解决数据中的非平衡问题.在回归层中,提出了一种分组扩展的训练策略,有效纠正了受非平衡影响而被错误分类的样本,从而进一步解决数据非平衡问题.此外,还基于贪心策略设计了一种概率加权方法,目的是对有把握的样本输出更加准确的预测.该模型在山东沿海14个地区的风速数据集上进行了验证,并与相关方法进行了比较,其中,订正后的风速预报整体和极端风速事件的平均绝对误差分别降低了34.4%和69.0%,表明该模型在提高极端风速事件预测能力的同时,也保持了对稳定事件的预测性能.

关键词: 风速预报订正, 层次模型, 数据非平衡, 极端风速预测

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

中图分类号:

TP181

曹阳,翟俊海,韩玲. 基于层次模型的非平衡风速预报订正[J]. 河北大学学报(自然科学版), 2025, 45(3): 317-326.

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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