Hybrid-Order Representation Learning for Electricity Theft Detection

Electricity theft is the primary cause of electrical losses in power systems, which severely harms the economic benefits of electricity providers and threatens the safety of the power supply. However, due to the inherent complex correlation and periodicity of electricity consumption and the low effi...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2023-02, Vol.19 (2), p.1248-1259
Main Authors: Zhu, Yuying, Zhang, Yang, Liu, Lingbo, Liu, Yang, Li, Guanbin, Mao, Mingzhi, Lin, Liang
Format: Article
Language:English
Subjects:
Anomalies
Computational modeling
Correlation
Correlation analysis
Data models
Data processing
Deep learning
Electricity
Electricity consumption
electricity theft detection
Feature extraction
Informatics
Learning
power system
Representation learning
Representations
second-order representation
smart grids
Theft
Training
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Summary:Electricity theft is the primary cause of electrical losses in power systems, which severely harms the economic benefits of electricity providers and threatens the safety of the power supply. However, due to the inherent complex correlation and periodicity of electricity consumption and the low efficiency of large-scale data processing, detecting anomalies in electricity consumption data accurately and efficiently remains challenging. Existing methods usually focus on first-order information and ignore the second-order representation learning that can efficiently model global temporal dependency and facilitate discriminative representation learning of electricity consumption data. In this article, we propose a novel electricity theft detection framework named hybrid-order representation learning network (HORLN). Specifically, the sequential electricity consumption data is transformed into the matrix format containing weekly consumption records. Then, an inter-and-intra week convolution block is designed to capture multiscale features in a local-to-global manner. Meanwhile, a self-dependency modeling module is proposed to learn the second-order representations from self-correlation matrices, which are finally combined with the first-order representations to predict the anomaly scores of electricity consumers. Extensive experiments on a real-world benchmark demonstrate the advantages of our HORLN over state-of-the-art methods.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3179243