基于损失函数改进和补丁时序Transformer网络的风功率超短期多步预测

晏吴宇歆; 张海波; 刘童蕙; 黄松涛; 尚国政

doi:doi:10.19912/j.0254-0096.tynxb.2024-1903

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基于损失函数改进和补丁时序Transformer网络的风功率超短期多步预测

更新时间：2025-12-02

- 基于损失函数改进和补丁时序Transformer网络的风功率超短期多步预测
- Vol. 46, Issue 6, Pages: 510-521(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1903
  CLC： TM731
- Published Online：07 July 2025，
  
  Published：2025
- 稿件说明：
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晏吴宇歆, 张海波, 刘童蕙, et al. 基于损失函数改进和补丁时序Transformer网络的风功率超短期多步预测[J]. 2025, 46(6): 510-521.
DOI：

晏吴宇歆, 张海波, 刘童蕙, et al. 基于损失函数改进和补丁时序Transformer网络的风功率超短期多步预测[J]. 2025, 46(6): 510-521. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1903.

摘要

为提高风电功率超短期多步预测的精度

提出一种基于损失函数改进和补丁（Patch）时序Transformer网络的风功率超短期多步预测模型。首先

通过风功率数据的图像异常检测与清洗算法进行数据预处理

提升数据质量;其次

为增强Transformer模型的鲁棒性并加强局部序列依赖的捕捉能力

在原始Transformer结构中引入补丁模块和通道独立策略。最后

为进一步过滤噪声并提高序列预测中的形状变化感知能力

设计一种新颖的多元非线性损失函数。实验结果表明

所提出的模型在多项误差指标上显著优于对比模型

有效提升了超短期风电功率的多步预测精度。

Abstract

To enhance the accuracy of ultra-short-term multi-step wind power forecasting

this study proposes a novel model that integrates improvements in the loss function with a patch-based temporal Transformer network. Specifically

an image-based anomaly detection and cleaning algorithm is firstly employed for data preprocessing

thereby enhancing the quality of the wind power data. Subsequently

to improve the robustness of the Transformer architecture and to strengthen its ability to capture local sequential dependencies

a patch module and a channel-independent strategy are incorporated into the standard Transformer framework. Finally

a novel multivariate nonlinear loss function is designed to effectively filter noise and to enhance the model's sensitivity to shape variations during sequence prediction. Extensive experimental results demonstrate that the proposed approach significantly outperforms several baseline models across multiple error metrics

thereby achieving substantial improvements in ultra-short-term multi-step wind power forecasting accuracy.

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Keywords

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