Electricity theft detection based on stacked sparse denoising autoencoder

Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper. The technical route is to employ the electricity data from honest users as the training samples, and the...

Full description

Saved in:
Bibliographic Details
Published in:International journal of electrical power & energy systems Vol. 125; p. 106448
Main Authors: Huang, Yifan, Xu, Qifeng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2021
Subjects:
Data reconstruction
Electricity theft detection
Feature extraction
Receiver operating characteristic curve
Stack autoencoder
Feature extraction
Data reconstruction
Receiver operating characteristic curve
Electricity theft detection
Stack autoencoder
ISSN:0142-0615, 1879-3517
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper. The technical route is to employ the electricity data from honest users as the training samples, and the autoencoder can learn the effective features from the data and then reconstruct the inputs as much as possible. For the anomalous behavior, since it contributes little to the autoencoder, the detector returns to a comparatively higher reconstruction error; hence the theft users can be recognized by setting an appropriate error threshold. To improve the feature extraction ability and the robustness, the sparsity and noise are introduced into the autoencoder, and the particle swarm optimization algorithm is applied to optimize these hyper-parameters. Moreover, the receiver operating characteristic curve is put forward to estimate the optimal error threshold. Finally, the proposed approach is evaluated and verified using the electricity dataset in Fujian, China.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2020.106448