An Open-Source Toolbox with Classical Classifiers for Electricity Theft Detection

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Abstrakt

Recently, there is increasing interest in detecting electricity thieves for economic benefits for power companies, and many works aim to improve the accuracy of electricity theft detection. Nevertheless, a core obstacle that currently hinders the direct comparison of classifiers for electricity theft detection is the lack of a standard and public dataset, since fraudulent power load profiles are usually difficult to collect for various reasons, including cost, cumber, and confidentiality. Therefore, this paper presents an open-source toolbox, which generates different kinds of fraudulent power load profiles from attack models, and integrates classical classifiers (e.g., support vector machine, multi-layer perceptron, convolutional neural network, long short-term memory, bidirectional long short-term memory) with different performance as baselines for the comparison with new algorithms. Users can easily generate datasets and modify parameters of classical classifiers guided by user friendly interactive interfaces. The codes, toolbox, and user manuals are available online and it is free to use and extend them.
OriginalsprogEngelsk
Titel2021 IEEE 2nd China International Youth Conference on Electrical Engineering (CIYCEE)
Antal sider7
ForlagIEEE
Publikationsdatodec. 2021
ISBN (Elektronisk)9781665400640
DOI
StatusUdgivet - dec. 2021
Begivenhed2nd IEEE China International Youth Conference on Electrical Engineering, CIYCEE 2021 - Chengdu, Kina
Varighed: 15 dec. 202117 dec. 2021

Konference

Konference2nd IEEE China International Youth Conference on Electrical Engineering, CIYCEE 2021
Land/OmrådeKina
ByChengdu
Periode15/12/202117/12/2021

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