基于CEEMD-GA-BiGRU-Attention的风电超短期功率预测研究

王梓涛; 刘莘轶; 王洋; 刘单珂; 于立军

doi:10.19912/j.0254-0096.tynxb.2025-0761

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基于CEEMD-GA-BiGRU-Attention的风电超短期功率预测研究

更新时间：2026-10-15

- 基于CEEMD-GA-BiGRU-Attention的风电超短期功率预测研究
- Vol. 47, Issue 9, Pages: 795-804(2026)
- 作者机构：
- 作者简介：
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- DOI：10.19912/j.0254-0096.tynxb.2025-0761
  CLC： TK89
- Online First：10 October 2026，
  
  Published：2026
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王梓涛, 刘莘轶, 王洋, et al. 基于CEEMD-GA-BiGRU-Attention的风电超短期功率预测研究[J]. 2026, 47(9): 795-804.
DOI：

王梓涛, 刘莘轶, 王洋, et al. 基于CEEMD-GA-BiGRU-Attention的风电超短期功率预测研究[J]. 2026, 47(9): 795-804. DOI： 10.19912/j.0254-0096.tynxb.2025-0761.

摘要

为提升超短期风电功率预测准确度、探索不同特征对功率预测模型的贡献

该文提出一种基于CEEMD-GA-BiGRU-Attention的风电超短期预测模型

并采用SHAP值分析方法分析各特征贡献。首先采用完全集成经验模态分解（CEEMD）方法分解功率数据

其次利用遗传算法（GA）优化带注意力机制的双向门控循环单元（BiGRU-attention）

对各模态分量动态建模

构建组合预测模型。将该模型与多种不同功率预测模型进行比较

结果表明其在RMSE、MAE、R2评价指标上具有优势

相较于BiGRU模型

R2提升10.08%

RMSE和MAE分别降低40.15%和42.38%。最后

采用SHAP值分析方法研究发现

对模型贡献较大的特征为历史实际功率和天气预报风速

前者在超短期预测初期占据主导地位

但重要性随时间步推进逐渐下降

后者的重要性则在预测中后期呈稳定上升趋势。

Abstract

To enhance the accuracy of ultra-short-term wind power prediction and investigate the contribution of diverse features to power prediction models

this paper introduces a wind power ultra-short-term prediction model grounded in CEEMD-GA-BiGRU-Attention. The model employs the SHAP value analysis method to assess the contribution of various features. Initially

the power data is decomposed using the Complete Ensemble Empirical Mode Decomposition （CEEMD） method. Subsequently

the Bidirectional Gated Recurrent Unit （BiGRU） model integrated with an attention mechanism is optimized using the Genetic Algorithm （GA） to dynamically model different modal components

thereby constructing a combined prediction model. When compared to various other power prediction models

this proposed model demonstrates superiority in terms of RMSE

MAE

and R evaluation metrics

with a notable 10.08% improvement in R compared to the BiGRU model

and a reduction of 40.15% and 42.38% in RMSE and MAE

respectively. Finally

through the application of the SHAP value analysis method

it is revealed that historical actual power and weather forecast wind speed are the features that significantly contribute to the model. Historical actual power dominates in the initial stages of ultra-short-term prediction

but its importance gradually diminishes over time

whereas the importance of weather forecast wind speed exhibits a stable upward trend in the middle to later stages.

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Keywords

references

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