基于小波卷积与Informer模型相结合的短期电力负荷预测

谢雄峰; 谭剑中; 何东; 岳汉文; 彭彪

doi:10.3969/j.issn.1008-0198.2026.01.013

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基于小波卷积与Informer模型相结合的短期电力负荷预测

电力人工智能与数字化 | 更新时间：2026-03-11

- 基于小波卷积与Informer模型相结合的短期电力负荷预测
- Short-Term Power Load Forecasting Based on WTC-Informer
- 湖南电力 2026年46卷第1期页码：98-106
- 作者机构：
  
  1. 湖南工业大学交通与电气工程学院,湖南,株洲,412007
  2. 国网湖南省电力有限公司株洲供电分公司,湖南,株洲,412007
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2026.01.013
  中图分类号： TM715.1
- 网络首发：2026-03-10，
  
  纸质出版：2026-03-10
- 稿件说明：
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谢雄峰, 谭剑中, 何东, 岳汉文, 彭彪. 基于小波卷积与Informer模型相结合的短期电力负荷预测[J]. 湖南电力, 2026, 46(1): 98-106.

XIE Xiongfeng, TAN Jianzhong, HE Dong, et al. Short-Term Power Load Forecasting Based on WTC-Informer[J]. 2026, 46(1): 98-106.
谢雄峰, 谭剑中, 何东, 岳汉文, 彭彪. 基于小波卷积与Informer模型相结合的短期电力负荷预测[J]. 湖南电力, 2026, 46(1): 98-106. DOI： 10.3969/j.issn.1008-0198.2026.01.013.

XIE Xiongfeng, TAN Jianzhong, HE Dong, et al. Short-Term Power Load Forecasting Based on WTC-Informer[J]. 2026, 46(1): 98-106. DOI： 10.3969/j.issn.1008-0198.2026.01.013.

摘要

随着风电、光伏等可再生能源大规模接入电网

电力系统运行的不确定性和波动性显著增强

负荷序列特征提取困难

导致短期电力负荷预测精度难以提升。针对此问题

提出一种基于小波卷积和Informer模型相结合的短期电力负荷预测模型

采用改进的变分模态分解（variational mode decomposition

VMD）

对数据分解降噪后输入小波卷积模块进行多级小波卷积

实现对复杂时间序列的多尺度特征提取及降低序列复杂度

从而提高预测精度。为验证模型的有效性

进行多组实验

结果表明

所提模型平均绝对百分比误差为1.893 1%

与单独使用Informer模型或仅使用GSWOA-VMD-Informer的方法相比降低了1.059 4个百分点和0.504 8个百分点

验证了该模型的有效性。

Abstract

With the large-scale integration of renewable energy sources such as wind power and photovoltaic power into the power grid

the uncertainty and volatility of the power system operation have significantly increased

making it difficult to extract load sequence features and improve the accuracy of short-term power load forecasting. To address this issue

a short-term power load forecasting model based on the combination of wavelet transform convolution(WTC) and Informer model is proposed. The improved variational mode decomposition(VMD) is used to decompose and denoise the input data before feeding it into the wavelet convolution module for multi-level wavelet convolution

achieving multi-scale feature extraction of complex time series and reducing sequence complexity

thereby improving the forecasting accuracy. To verify the validity of the model

multiple sets of experiments are conducted. The experimental results show that the Mean Absolute Percentage Error(MAPE) of the model proposed in this paper is 1.893 1%. Compared with the methods of using the Informer model alone or only using GSWOA-VMD-informer

it is reduced by 1.059 4% and 0.504 8%

respectively

verifying the effectiveness of this model.

关键词

Keywords

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