vachelhu / hebr

Hierarchical Electricity-theft Behavior Recognition, WWW 2020

HEBR

This project implements the method of hierarchical electricity-theft behavior recognition proposed in [1], which is an algorithm for detecting electricity theft.

Testing

This project is implemented in Python 3.6

Dependency:

• Python 3.6. Version 3.6.2 has been tested.
• Tensorflow. Version 1.2.0 has been tested. Note that the GPU support is encouraged as it greatly boosts training efficiency.
• Other Python modules. Some other Python module dependencies are listed in requirements.txt, which can be easily installed with pip pip install -r requirements.txt

Testing the Project:

python run.py -f ./data/ -o ./data/result.csv

Usage

Given an array of multi-source time series data, including users' electricity records, non-technical losses in transformer area, temperature records, this program can be used to estimate the electricity-theft probabilities for users.

Input Format

The input files are expected to be four parts:

(1) Electricity Usage Records: a CSV file, which contains daily records for every user: #userID, #areaID, #date, #total electricity, #top electricity, #on-peak electricity, #flat electricity, #off-peak electricity.

(2) Non-Technical Loss Records: a CSV file, which contains daily records for every area: #areaID, #date, #cost electricity, #billed electricity, #lost electricity. Here, the areaID corresponds to the areaID in (1)

(3) Temperature Records: a CSV file, which records daily weather, containing #areaID, #data, #high temperature, #low temperature. We can spider the data by the code in ./data_factory/temperature_spider.py

(4) Labels of Electricity Thieves: a CSV file, which records the time that the electricity thieves are caught, containing #userID, #areaID, #date.

Output Format

The program outputs to a file named result.csv which contains the results of electricity-theft probabilities estimated by HEBR.

Main Script

The help of main script can be obtained by excuting command:

python run.py -h
usage: run.py [-h] [-f DATA_FILE] [-o OUTPUT_FILE] [-hl HISTORY_LENGHT]
              [-b BATCH_SIZE] [-e NUM_EPOCH] [-n CPU_JOBS] [-g GPU_ID]
              [-l LEARNING_RATE] [-iu USER_DIMS] [-il NTL_DIMS]
              [-ie CLIMATE_DIMS] [-iul USER_NTL_DIMS] [-iue USER_CLIMATE_DIMS]
              [-iule USER_NTL_CLIMATE_DIMS]

optional arguments:
  -h, --help            show this help message and exit
  -f DATA_FILE, --data_file DATA_FILE
                        path of input file
  -o OUTPUT_FILE, --output_file OUTPUT_FILE
                        path of output file
  -hl HISTORY_LENGHT, --history_lenght HISTORY_LENGHT
                        the historical length for observed data, default value
                        is 180
  -b BATCH_SIZE, --batch_size BATCH_SIZE
                        the number of samples in each batch, default value is
                        1000
  -e NUM_EPOCH, --num_epoch NUM_EPOCH
                        number of epoch, default value is 100
  -n CPU_JOBS, --cpu_jobs CPU_JOBS
                        number of cpu jobs, default value is maximum number of
                        cpu kernel
  -g GPU_ID, --gpu_id GPU_ID
                        index of gpu, default value is 0
  -l LEARNING_RATE, --learning_rate LEARNING_RATE
  -iu USER_DIMS, --user_dims USER_DIMS
                        dimension of micro-level memory matrix, default value
                        is 16
  -il NTL_DIMS, --ntl_dims NTL_DIMS
                        dimension of meso-level memory matrix, default value
                        is 4
  -ie CLIMATE_DIMS, --climate_dims CLIMATE_DIMS
                        dimension of macro-level memory matrix, default value
                        is 8
  -iul USER_NTL_DIMS, --user_ntl_dims USER_NTL_DIMS
                        dimension of user-area memory matrix, default value is
                        64
  -iue USER_CLIMATE_DIMS, --user_climate_dims USER_CLIMATE_DIMS
                        dimension of user-climate memory matrix, default value
                        is 64
  -iule USER_NTL_CLIMATE_DIMS, --user_ntl_climate_dims USER_NTL_CLIMATE_DIMS
                        dimension of user_ntl_climate memory matrix, default
                        value is 256

Reference

[1] Wenjie, H; Yang, Y; Jianbo, W; Xuanwen, H and Ziqiang, C, 2020, Understanding Electricity-Theft Behavior via Multi-Source Data, In WWW, 2020

 @inproceedings{hu2020theft, 
    title={Understanding Electricity-Theft Behavior via Multi-Source Data},
    author={Wenjie Hu and Yang Yang and Jianbo Wang and Xuanwen Huang and Ziqiang Cheng},
    booktitle={Proceedings of WWW},
    year={2020}
    }