基于TCN-SENet-Transformer的新型超短期风电功率组合预测方法

殷钰朋; 匡洪海; 李星宇; 曹世鹏; 杨慧娴

doi:doi:10.19912/j.0254-0096.tynxb.2024-1314

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基于TCN-SENet-Transformer的新型超短期风电功率组合预测方法

更新时间：2026-01-08

- 基于TCN-SENet-Transformer的新型超短期风电功率组合预测方法
- Vol. 46, Issue 12, Pages: 735-741(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1314
  CLC： TM614
- Published Online：07 January 2026，
  
  Published：2025
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殷钰朋, 匡洪海, 李星宇, et al. 基于TCN-SENet-Transformer的新型超短期风电功率组合预测方法[J]. 2025, 46(12): 735-741.
DOI：

殷钰朋, 匡洪海, 李星宇, et al. 基于TCN-SENet-Transformer的新型超短期风电功率组合预测方法[J]. 2025, 46(12): 735-741. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1314.

摘要

针对传统风电功率预测方法在时序与空间全局特征的联系紧密性及并行处理方面考虑较少

预测可靠性有限的问题

提出基于交叉注意力融合时空特征的超短期风电功率预测方法（TCN-SENet-Transformer）。首先

利用压缩激励网络（SENet）调整通道特征权重

并通过时域卷积网络（TCN）捕捉数据的空间特征;同时

采用Transformer识别多特征数据的长期时序特征;然后

引入交叉注意力机制（CA）融合时序和空间特征;最后

以中国某风电场的实际数据进行超短期风电功率预测

并与其他预测模型进行对比

算例分析结果表明

所提组合预测模型可有效提高预测精度。

Abstract

Aiming at the problems that traditional wind power forecasting methods have few considerations in terms of the close relationship between time series and spatial global features and parallel processing

and the prediction reliability is limited

an ultra-short term wind power forecasting method based on cross-attention fusion of spatial-temporal features of TCN-SENet-Transformer is proposed. Firstly

squeeze and excitation networks（SENet） are used to adjust the channel feature weights

and temporal convolutional networks（TCN） are used to capture the spatial features of the data. Meanwhile

Transformer is used to identify the long-term timing characteristics of multi-feature data. Then

cross-attention（CA） is introduced to integrate temporal and spatial features. Finally

the actual data of a wind farm in China is used to forecast ultra-short term wind power

and the comparison is made with other prediction models. The results of example analysis show that the proposed combined prediction model effectively improves the prediction accuracy.

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Keywords

references

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