Electricity Theft Detection using Machine Learning

Abstract

This research work dealt with the indiscriminate theft of electric power, reported as a non-technical loss, affecting electric distribution companies and customers, triggering serious consequences including fires and blackouts. The research focused on recommending the best prediction model using Machine Learning in electrical energy theft. The source of the information on the electricity consumption of 42372 consumers was a dataset published in the State Grid Corporation of China. The method used was data imputation, data balancing (oversampling and under sampling), and feature extraction to improve energy theft detection. Five Machine Learning models were tested. As a result, the accuracy indicator of the SVM model was 81%, K-Nearest Neighbors 79%, Random Forest 80%, Logistic Regression 69%, and Naive Bayes 68%. It is concluded that the best performance, with an accuracy of 81%, is obtained by using the SVM model.

Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review

“…In order to resolve the problem of missing values in an energy theft dataset, various interpolation methods have been employed, and they can be categorized into two techniques: linear [62] and polynomial [63,64] methods. Based on the simple linear algorithm, zero and average values can be used to replace missing values to recover the energy consumption data over a period [62]. Polynomial interpolation may be effective when the derivatives between data points approach tend to follow certain polynomial expressions [63,64].…”

Electrical Network Fraud Detection Using a CNN+LSTM Model

“…Graphical representation of the CNN-GRU-CS architecture used in the study "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid". It is important to emphasize that despite the recent focus on neural networks, efforts have also been made with simpler architectures that have had excellent results, such as the support vector models architectures presented by Petrlik et al (2022) and decision tree architectures presented by Appiah et al (2023) in the study "Extremely randomized trees machine learning model for electricity theft detection." In the latter, the use of the grid search optimization technique to optimize the hyperparameters of the proposed model stands out, as well as the fact of very promising metric yields (98% accuracy, 98% F1, 95% Matthew correlation.…”