华东师范大学学报（自然科学版） >

2021 , Vol. 2021 >Issue 5: 104 - 114

DOI: https://doi.org/10.3969/j.issn.1000-5641.2021.05.010

数据分析与应用

基于t-LeNet与时间序列分类的窃电行为检测

  • 马晓琴 ,
  • 薛晓慧 ,
  • 罗红郊 ,
  • 刘通宇 ,
  • 袁培森
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  • 1. 国网青海省电力公司 信息通信公司, 西宁　810008
    2. 南京农业大学 人工智能学院, 南京　210095
马晓琴, 女, 高级工程师, 研究方向为用电信息系统检修维护. E-mail: xqm8651@126.com

收稿日期: 2021-08-07

  网络出版日期: 2021-09-28

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Electricity theft detection based on t-LeNet and time series classification

  • Xiaoqin MA ,
  • Xiaohui XUE ,
  • Hongjiao LUO ,
  • Tongyu LIU ,
  • Peisen YUAN
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  • 1. Information and Communication Company, State Grid Qinghai Province Electric Power Company, Xining　810008 China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing　210095, China

Received date: 2021-08-07

  Online published: 2021-09-28

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摘要

窃电行为是导致电力企业电能与经济效益损失的重要原因. 提出了一种基于t-LeNet(Time-Series Specific Version of LeNet Model)与时间序列分类(Time Series Classification, TSC)的窃电行为检测方法: 首先, 获取用户用电量时序数据, 使用降采样方法生成训练集; 然后, 使用t-LeNet神经网络训练并预测得到分类结果, 判断用户是否存在窃电行为. 使用国家电网真实用户的用电量数据集进行了实验验证. 实验结果表明, 所提方法相较于基于Time-CNN(Time Convolutional Neural Network)、MLP(Muti-Layer Perception)的时间序列分类方法, 在综合评价指标、精确率、召回率指标上均有不同程度提高, 其对窃电行为的检测具有可行性与有效性.

关键词： 时间序列分类; t-LeNet; 窃电检测

本文引用格式

马晓琴 , 薛晓慧 , 罗红郊 , 刘通宇 , 袁培森 . 基于t-LeNet与时间序列分类的窃电行为检测[J]. 华东师范大学学报（自然科学版）, 2021 , 2021(5) : 104 -114 . DOI: 10.3969/j.issn.1000-5641.2021.05.010

Abstract

Electricity theft results in significant losses in both electric energy and economic benefits for electric power enterprises. This paper proposes a method to detect electricity theft based on t-LeNet and time series classification. First, a user’s power consumption time series data is obtained, and down-sampling is used to generate a training set. A t-LeNet neural network can then be used to train and predict classification results for determining whether the user exhibits behavior reflective of electricity theft. Lastly, real user power consumption data from the state grid can be used to conduct experiments. The results show that compared with the time series classification method based on Time-CNN (Time Convolutional Neural Network) and MLP (Muti-Layer Perception), the proposed method offers improvements in the comprehensive evaluation index, accuracy rate, and recall rate index. Hence, the proposed method can successfully detect electricity theft.

Key words： time series classification; t-LeNet; electricity theft detection

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