考虑相似日和误差修正的TETransformer超短期负荷功率预测

李练兵; 高一波; 吴伟强; 魏玉憧; 代亮亮; 高国强

doi:doi:10.19912/j.0254-0096.tynxb.2024-1608

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考虑相似日和误差修正的TETransformer超短期负荷功率预测

更新时间：2026-02-06

- 考虑相似日和误差修正的TETransformer超短期负荷功率预测
- Vol. 47, Issue 1, Pages: 301-312(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1608
  CLC： TM714
- Online First：05 February 2026，
  
  Published：05 February 2026
- 稿件说明：
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李练兵, 高一波, 吴伟强, et al. 考虑相似日和误差修正的TETransformer超短期负荷功率预测[J]. 2026, 47(1): 301-312.
DOI：

李练兵, 高一波, 吴伟强, et al. 考虑相似日和误差修正的TETransformer超短期负荷功率预测[J]. 2026, 47(1): 301-312. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1608.

摘要

为进一步提高超短期电力负荷的预测精度

增强对电力负荷时序特征的提取能力

提出一种考虑相似日与误差修正的时序增强Transformer（TETransformer）超短期电力负荷预测方法。首先

利用灰色关联分析选取气象相似日;然后

在Transformer模型基础上构造局部时序增强注意力机制

利用时序卷积提高注意力机制的局部时序特征感知能力

聚合观测点临近区域相关信息;传统Transformer模型中嵌入时序卷积层

扩展特征图

在Transformer模型全局信息提取的基础上增强局部时序信息提取能力;最后

将历史特征数据和未来气象数据输入TETransforemr

气象相似日的负荷功率序列输入LSTM

通过全连接层融合历史时序特征与相似日信息

引入基于编码器的误差修正模块

提高模型预测精度。通过多模型对比与消融实验

预测精度均有提高

证明所提方法可有效增强对电力负荷的提取能力

在超短期电力负荷领域具有一定的应用意义。

Abstract

In order to further improve the prediction accuracy of ultra-short-term power load and enhance the extraction ability of power load temporal features

a temporal enhancement Transformer （TETransformer） ultra-short-term power load prediction method considering similar days and error correction is proposed. First

the meteorologically similar days are selected using gray relational analysis

and then

the local temporal enhancement attention mechanism is constructed on the basis of the Transformer model

and the temporal convolution is used to improve the local temporal feature perception ability of the attention mechanism

and to aggregate the relevant information of the adjacent area of the observation point; the traditional Transformer model is embedded with a temporal convolution layer

and the feature map is extended

in which the global information of the Transformer model is not available to the public. traditional Transformer model to extend the feature map and enhance the local time-series information extraction capability on the basis of the global information extraction of the Transformer model. Finally

the historical feature data and future meteorological data are input to TETransforemr

and the load power sequences of meteorologically similar days are input to LSTM

and the historical time-series features and similar day information are fused through the fully-connected layer

and the encoder-based error correction module is introduced to improve the model prediction accuracy. Through the multi-model comparison and ablation experiments

the prediction accuracies are improved

which proves that the proposed method can effectively enhance the extraction ability of power load and has certain application significance in the field of ultra-short-term power load.

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references

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