基于VMD-TCN-GRU-AM的超短期风电预测

范竞敏; 贺广林; 王新刚; 张奎; 李佩怡; 何梓秋

doi:doi:10.19912/j.0254-0096.tynxb.2024-0291

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基于VMD-TCN-GRU-AM的超短期风电预测

更新时间：2025-12-02

- 基于VMD-TCN-GRU-AM的超短期风电预测
- Vol. 46, Issue 6, Pages: 538-547(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0291
  CLC： TM614
- Published Online：07 July 2025，
  
  Published：2025
- 稿件说明：
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范竞敏, 贺广林, 王新刚, et al. 基于VMD-TCN-GRU-AM的超短期风电预测[J]. 2025, 46(6): 538-547.
DOI：

范竞敏, 贺广林, 王新刚, et al. 基于VMD-TCN-GRU-AM的超短期风电预测[J]. 2025, 46(6): 538-547. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0291.

摘要

为提升风电功率预测精度并解决单一神经网络模型在预测波动性和间歇性风电数据时的滞后性问题

提出一种融合变分模态分解（VMD）、时间卷积网络（TCN）、门控循环单元（GRU）和注意力机制（AM）的组合预测模型。模型利用VMD将原始风电数据分解为具有不同中心频率的本征模态函数（IMF）

以降低数据的随机性和波动性。然后通过TCN-GRU-AM模型对这些IMF子序列进行独立预测

TCN和GRU的组合能更好地捕捉各子序列的复杂特征和时间依赖性

AM则可增强模型对时间序列中关键时间步的识别能力。最终通过叠加预测分量重构出风电功率预测结果。实验验证显示

该模型能显著提高预测精度且有效缓解了滞后现象。

Abstract

To enhance the accuracy of wind power forecasting and solve the lag in single neural network models when predicting fluctuating and intermittent wind power data

this paper proposes a hybrid forecasting model integrating Variational Mode Decomposition （VMD）

Temporal Convolutional Networks （TCN）

Gated Recurrent Units （GRU）

and Attention Mechanism （AM）. The model employs VMD to decompose raw wind power data into Intrinsic Mode Functions （IMFs） with different central frequencies

decreasing the data's stochasticity and volatility. Subsequently

the TCN-GRU-AM model independently predicts these IMF subsequence. The combination of TCN and GRU effectively captures the complex features and temporal dependencies within each subsequence

while AM boosts the model's capacity to recognize crucial time steps in time series data. Ultimately

the predicted components are superimposed and reconstructed to yield the final wind power prediction outcome. Experimental results demonstrate that this model significantly enhances forecasting precision and effectively mitigates the lag phenomenon.

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Keywords

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