Short-term power load forecasting based on load decomposition and identification

朱俊澎; 李子钰; 李虎军; 邓振立; 袁 越

doi:10.3969/j.issn.1009-1831.2025.02.009

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Short-term power load forecasting based on load decomposition and identification

更新时间：2026-03-05

- Short-term power load forecasting based on load decomposition and identification
- Vol. 27, Issue 2, (2025)
- 作者机构：
  
  1. 河海大学电气与动力工程学院,南京,210098
  2. 国网河南省电力公司经济技术研究院,郑州,450052
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.02.009
  CLC：
- Published：2025
- 稿件说明：
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朱俊澎, 李子钰, 李虎军, et al. Short-term power load forecasting based on load decomposition and identification[J]. 2025, 27(2).
DOI：

朱俊澎, 李子钰, 李虎军, et al. Short-term power load forecasting based on load decomposition and identification[J]. 2025, 27(2). DOI： 10.3969/j.issn.1009-1831.2025.02.009.

摘要

为进一步降低电力负荷数据预测误差

提出一种基于负荷分解与辨识的负荷短期预测方法。首先

针对各行业电力负荷数据

以温度敏感负荷与温度序列的多项式拟合误差为目标函数

将负荷分解转化为数学优化问题

将各行业总负荷分解为周度基荷分量和温度敏感负荷分量；其次

基于长短时记忆网络对温度敏感负荷分量进行短期负荷预测；最后

将温敏负荷预测结果与周度基荷分量叠加得到完整的负荷预测结果。采用某省2022年分行业电力负荷数据进行验证

结果表明提出的基于负荷分解与辨识的短期电力负荷预测方法可有效降低短期负荷预测误差。

Abstract

In order to further reduce the forecasting error of electric load data

a short-term power load forecasting method based on load decomposition and identification is proposed. First

for the electric power load data of each industry

the polynomial fitting error of temperature-sensitive load to the temperature series is taken as the objective function

and the load decomposition is transformed into a mathematical optimization problem

and the total load of each industry is decomposed into the weekly load based on load identification component and the temperature-sensitive load component. Second

the short-term load prediction is performed for the temperature-sensitive load component based on the long short-term memory network. Finally

the temperature-sensitive load prediction results are superimposed with the weekly load based on load identification component to obtain the complete load forecast results. The results show that the short-term load forecasting method based on load decomposition and identification proposed can effectively reduce the short-term load forecasting error.

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