窃电检测数据集和工具箱（An Open-Source Data

This is the user manual of a paper named "An Open-Source Toolbox with Classical Classifiers for Electricity Theft Detection" 

1. English version manual

Download the toolbox linek:  https://pan.baidu.com/s/1j-mmnNbwkd7nKSeJb3Rrkg

password: 1234

It includes four files. After the user selects all the files, tap the Download button.

Toolbox: A group of .fig files with the graphical user interface (GUI) and a set of .m files with basic codes of the data generator and classical classifiers.

Raw data: real household power load profiles from block 1 of smart meters in low carbon London.

Generated data: The authors have used the raw data to generate the dataset. The user can use the author's data.

Original codes:  a set of .m files. Toolbox is relatively inflexible, and users can modify the structure of the classifier with source code.

Video Instructions links: https://www.bilibili.com/video/BV1ZU4y1A7HS?spm_id_from=333.999.0.0

If you have any questions, you can email me at: 851282212@qq.com

2. Chinese version manual（中文版用户手册）

这是“An Open-Source Toolbox with Classical Classifiers for Electricity Theft Detection”这篇会议论文的用户说明书。教读者如何利用工具箱生成窃电数据集。

工具箱下载链接: https://pan.baidu.com/s/1j-mmnNbwkd7nKSeJb3Rrkg

密码: 1234

它包括4个子文件，点击下载即可。

Toolbox: 一组GUI文件和对应的m文件。这是工具箱的全部文件。

Raw data: 伦敦智能电表数据集中的其中一个数据，来源于第1个街区。

Generated data:这是作者事前，用raw data生成的数据集。读者可以用这个数据集。

Original codes:  由于toolbox中的分类器网络结构没那么灵活，读者可以对源代码进行直接修改。

中文版视频操作手册链接: https://www.bilibili.com/video/BV1Cf4y1F7HZ?spm_id_from=333.999.0.0

假如你有疑问，可以发邮件给我：851282212@qq.com