基于t-LeNet与时间序列分类的窃电行为检测

doi:10.3969/j.issn.1000-5641.2021.05.010

华东师范大学学报（自然科学版） ›› 2021, Vol. 2021 ›› Issue (5): 104-114.doi: 10.3969/j.issn.1000-5641.2021.05.010

基于t-LeNet与时间序列分类的窃电行为检测

马晓琴¹(), 薛晓慧¹, 罗红郊¹, 刘通宇², 袁培森^2,*()

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

收稿日期:2021-08-07 出版日期:2021-09-25 发布日期:2021-09-28
通讯作者: 袁培森 E-mail:xqm8651@126.com;peiseny@njau.edu.cn
作者简介: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

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

Xiaoqin MA¹(), Xiaohui XUE¹, Hongjiao LUO¹, Tongyu LIU², Peisen YUAN^2,*()

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:2021-08-07 Online:2021-09-25 Published:2021-09-28
Contact: Peisen YUAN E-mail:xqm8651@126.com;peiseny@njau.edu.cn

摘要/Abstract

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

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

中图分类号:

TP391

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.

Xiaoqin MA, Xiaohui XUE, Hongjiao LUO, Tongyu LIU, Peisen YUAN. Electricity theft detection based on t-LeNet and time series classification[J]. Journal of East China Normal University(Natural Science), 2021, 2021(5): 104-114.

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层数	名称	参数设置
1	Input
2	Convolution 1	filter = 5, kernel_size = 5, activation = ‘relu’, padding = ‘same’
3	Pooling 1	Max Pooling, pool size = 2
4	Convolution 2	filter = 20, kernel_size = 5, activation = ‘relu’, padding = ‘same’
5	Pooling 2	Max Pooling, pool size = 4
6	Flatten
7	Dense	uints = 500, activation = ‘relu’
8	Output(Dense)	uints = n, activation = ‘softmax’

模型	实验参数
模型	输入序列长度	批量大小	迭代次数	层数
Time-CNN	1034	16	50	4
MLP	1034	16	50	4
t-LeNet	1034	16	50	5

模型	学习率区间	单次增长幅度	最高准确率	最高准确率对应学习率
Time-CNN	[0.01, 0.10]	0.010	0.7123	0.020
Time-CNN	[0.001, 0.010]	0.001	0.5138	0.006
MLP	[0.001, 0.010]	0.001	0.6542	0.008
MLP	[0.01, 0.20]	0.01	0.6736	0.050
t-LeNet	[0.01, 0.10]	0.01	0.6971	0.010 00
	[0.001, 0, 010]	0.001	0.700 6	0.001 00
	[0.0001, 0.0010]	0.000 1	0.7013	0.00010
	[0.00001, 0.00010]	0.000 01	0.712 8	0.000 07

模型	学习率	A	${P_{ {\text{macro} } } }$	$ {R_{{\text{macro}}}} $	$F_{1{ {\text{macro} } } }$
Time-CNN	0.002	0.7123	0.7139	0.7129	0.7121
MLP	0.050	0.6763	0.7322	0.6803	0.6586
t-LeNet	0.00007	0.7128	0.7258	0.7218	0.7212

模型	A	${P_{ {\text{macro} } } }$	$ {R_{{\text{macro}}}} $	$F_{1{ {\text{macro} } } }$
Time-CNN	0.7137	0.6784	0.7020	0.6900
MLP	0.6867	0.5909	0.5941	0.5925
t-LeNet	0.7289	0.6905	0.7122	0.7012

基于t-LeNet与时间序列分类的窃电行为检测

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

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