新型电力系统中一种基于LSTM和CNN的倾斜样本预测算法

张琦; 李志浩; 范叶平; 马剑波

doi:10.11959/j.issn.1000-0801.2025182

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新型电力系统中一种基于LSTM和CNN的倾斜样本预测算法

工程与应用 | 更新时间：2025-11-06

- 新型电力系统中一种基于LSTM和CNN的倾斜样本预测算法
- An unbalanced sample prediction algorithm based on LSTM and CNN in the new power systems
- 电信科学 2025年41卷第10期页码：210-221
- 作者机构：
  
  安徽继远软件有限公司，安徽合肥 230093
- 作者简介：
  
  [ "张琦（1995- ），男，安徽继远软件有限公司助理工程师，主要研究方向为深度学习与图像识别。" ]
  [ "李志浩（1981- ），男，安徽继远软件有限公司工程师，主要研究方向为电力数字孪生技术、电力物联网及量子通信。" ]
  [ "范叶平（1979- ），男，安徽继远软件有限公司高级工程师，主要研究方向为电力数字建模、数字孪生基础技术平台及大数据分析。" ]
  [ "马剑波（1998- ），男，安徽继远软件有限公司工程师，主要研究方向为电力人工智能及大数据分析。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025182
  中图分类号： TP183
- 收稿：2025-02-02，
  
  修回：2025-08-05，
  
  纸质出版：2025-10-20
- 稿件说明：
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张琦,李志浩,范叶平等.新型电力系统中一种基于LSTM和CNN的倾斜样本预测算法[J].电信科学,2025,41(10):210-221.

ZHANG Qi,LI Zhihao,FAN Yeping,et al.An unbalanced sample prediction algorithm based on LSTM and CNN in the new power systems[J].Telecommunications Science,2025,41(10):210-221.
张琦,李志浩,范叶平等.新型电力系统中一种基于LSTM和CNN的倾斜样本预测算法[J].电信科学,2025,41(10):210-221. DOI： 10.11959/j.issn.1000-0801.2025182.

ZHANG Qi,LI Zhihao,FAN Yeping,et al.An unbalanced sample prediction algorithm based on LSTM and CNN in the new power systems[J].Telecommunications Science,2025,41(10):210-221. DOI： 10.11959/j.issn.1000-0801.2025182.

摘要

基于电力数字空间技术体系，提出了一种基于特征关联性分析与改进卷积神经网络（convolutional neural network，CNN）-长短期记忆（long short-term memory，LSTM）网络的预测算法。首先，在注意力层，对用户关联性进行分析，将与电力负荷用户强相关的影响因素进行加权求平均，调整这些类别在用户电力负荷预测中的权重大小，加强少且重要的电力样本特征对电力负荷算法的影响并避免冗余特征的干扰，实现特征选择；其次，在CNN层，通过构建2层神经网络，提取样本的空间特征，并将该空间特征输入LSTM网络中；最后，通过LSTM网络层提取序列的时间特征，输出预测结果。实验基于北方某城市供应电力资源的6个重要用户的保密数据进行预测，实验结果表明，基于关联性分析与改进CNN-LSTM模型的MAPE在5类用户测试的误差均低于LSTM、随机森林和BP神经网络的误差。

Abstract

A prediction algorithm based on feature correlation analysis and improved CNN (convolutional neural network)-LSTM (long short-term memory) network was proposed based on the power digital space technology system. Firstly

in the attention layer

the user correlation was analyzed

and the influencing factors strongly related to power load users were weighted and averaged. The weight of these categories in user power load prediction was adjusted to strengthen the influence of few and important power sample features on power load calculation and avoid the interference of redundant features

achieving feature selection. Secondly

in the CNN layer

by constructing a two-layer neural network

the spatial features of the samples are extracted and inputted into LSTM. Finally

LSTM extract the temporal features of the sequence through the LSTM layer and output the prediction results. The experiment was based on confidential data from six important users supplying power resources in a northern city for prediction. The experimental results show that the MAPE based on correlation analysis and improved CNN-LSTM model was lower than the error of LSTM

random forest

and BP neural network in five types of user tests.

关键词

Keywords

references

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