基于SSA-VMD预处理的TCN-Informer短期风速多步预测混合模型

孔宪正; 黄国勇; 邓为权; 刘发炳

doi:doi:10.19912/j.0254-0096.tynxb.2024-1806

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基于SSA-VMD预处理的TCN-Informer短期风速多步预测混合模型

更新时间：2026-03-06

- 基于SSA-VMD预处理的TCN-Informer短期风速多步预测混合模型
- Vol. 47, Issue 2, Pages: 527-538(2026)
- 作者机构：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1806
  CLC： TM614
- Online First：05 March 2026，
  
  Published：05 March 2026
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孔宪正, 黄国勇, 邓为权, et al. 基于SSA-VMD预处理的TCN-Informer短期风速多步预测混合模型[J]. 2026, 47(2): 527-538.
DOI：

孔宪正, 黄国勇, 邓为权, et al. 基于SSA-VMD预处理的TCN-Informer短期风速多步预测混合模型[J]. 2026, 47(2): 527-538. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1806.

摘要

针对传统风速预测方法多步预测准确性不足的问题

提出一种基于奇异谱分析和变分模态分解预处理的时间卷积网络-Informer混合预测模型。首先

利用奇异谱分析抑制原始风速中的噪声

降低风速的不稳定性;然后

利用变分模态分解降低风速序列的复杂度

并将各分量分别输入到时间卷积网络提取时间特征以加强局部信息的捕捉;最后

将各模态分量及其时空特征进行融合

输入到Informer自注意力模型对其长时间依赖关系进行建模

得到多步风速预测结果。以云南某风电场测风塔实测风速为验证

该模型在6步和12步预测上MAPE分别仅为1.63%和2.25%

进一步提高了短期风速多步预测准确性。

Abstract

To address the limited accuracy of multi-step wind speed predictions using conventional methods

this paper proposes a hybrid model that integrates Singular Spectrum Analysis （SSA） and Variational Mode Decomposition （VMD） with a Time Convolutional Network （TCN）-Informer architecture. Firstly

SSA is used to suppress noise in the original wind speed data and reduce its instability. Next

VMD is employed to reduce the complexity of the wind speed sequence

with each component then input into TCN's feature extraction module to capture temporal features and enhance local information representation. Finally

by fusing temporal and spatial features from each modal component and inputting them into Informer's self-attention model

long-term dependence relationships are modeled to obtain multi-step wind speed predictions. The proposed model was validated using measured wind speed data from a meteorological tower at a wind farm in Yunnan province

China. The results show that the MAPE for 6-step and 12-step predictions were only 1.63% and 2.25%

respectively

demonstrating significantly accuracy in short-term multi-step wind speed prediction.

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