Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

Electricity theft has been a major concern to the secure operation of power systems and the interests of power companies. Due to the different methods and types of electricity theft behaviors, it is difficult to determine the suspicion levels of consumers in the research of electricity theft detecti...

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Bibliographic Details
Main Authors: Guoying Lin, Xiaofeng Feng, Wenchong Guo, Xueyuan Cui, Shengyuan Liu, Weichao Jin, Zhenzhi Lin, Yi Ding
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Electricity theft
class imbalance
suspicion level
feature extraction
machine learning
random forest
Online Access:https://ieeexplore.ieee.org/document/9530415/

Internet

https://ieeexplore.ieee.org/document/9530415/

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