Work

Data-Driven Insights: Boosting Algorithms to Uncover Electricity Theft Patterns in AMI

Year: 2025

Type: article

Abstract: This study introduces a sophisticated supervised machine learning method for electric theft detection utilizing a customized Histogram Gradient Boosting (HGB) algorithm. Comprehensive preprocessing, including imputation, normalization, outlier management, and resampling, ensures the timeseries data is accurately prepared for analysis. The SMOTE-ENN algorithm corrects class imbalances, preparing the data for the feature optimization stage where crucial features are selected and extracted. The HGB algorithm, enhanced through Bayesian optimization, is central to the training process, resulting in a model that precisely classifies electricity consumption patterns as genuine or fraudulent. The robustness of the model is assessed against other recognized boosting methods, such as Adaptive Boosting (ADB), Gradient Boosting Decision Tree (GBDT), and LightGBM, alongside various ensemble and traditional machine learning models. Utilizing key performance metrics like accuracy, F1 score, and AUC for validation, the proposed model yields very promising results, with a 93% accuracy, 95% F1 score, and 98% AUC, outperforming the comparison group under similar dataset and hyperparameter conditions. This underscores the model's potential as a highly accurate tool for combating electricity theft within an advanced metering infrastructure (AMI).

Source: IEEE Transactions on Instrumentation and Measurement

DOI: 10.1109/tim.2025.3557097

Authors: Inam Ullah Khan, Arshid Ali, C. James Taylor, Xiandong Ma

Institutions: Southern Methodist University, South Dakota State University, Lancaster University

Language: English

Topic: Electricity Theft Detection Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,645