Research on Characteristics of Electricity Theft Behavior and Risk Early Warning Technology Based on Big Data Information of the Internet of Things

被引：1

作者：

Huang, Rongguo ^{[1
]}

Lu, Chunguang ^{[1
]}

Hu, Shuhong ^{[1
]}

Tang, Di ^{[1
]}

Pan, Boliang ^{[2
]}

机构：

[1] State Grid Zhejiang Elect Power Co Ltd, Mkt Serv Ctr, Hangzhou, Peoples R China

[2] Zhejiang Huayun Informat Technol Co Ltd, Hangzhou, Peoples R China

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, BIG DATA AND ALGORITHMS (EEBDA) | 2022年

关键词：

electricity consumption information collection system; big data; anti-stealing; structured model;

D O I：

10.1109/EEBDA53927.2022.9744829

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Based on the results of online loss calculations, we comprehensively utilize the existing massive data of power grid companies, and conduct in-depth mining of abnormal line loss rate lines or stations by using big data technologies such as Hadoop offline distributed computing and Spark memory computing to identify possible occurrences. Electricity stealing, providing electricity theft warning and data support services for power supply companies' anti-electricity theft inspections. The data collected on the spot proves the effectiveness of the structured anti-theft model, and provides an effective method for solving the problem of monitoring the behavior of electricity theft under the condition of big data.

引用

页码：123 / 127

页数：5

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