July 12, 2025

Improving Electricity Theft Detection with a Stacked Ensemble Model

Key Points

The stacked ensemble model significantly enhances detection of electricity theft, improving utility revenue.
Using the ADASYN method, the model effectively balances data samples, aiding performance metrics like Recall.
The approach incorporates random forest and gradient boosting techniques, generating strong predictive capabilities.
Evaluation metrics like ROC AUC confirm model effectiveness, highlighting the potential for application in utility management.

Abstract

Energy theft is a major problem for utility providers as it results in huge revenue loss and interruption in power supply. It is shown that traditional detection models cannot adequately cope with different methods of theft and unequal data distributions. This work provided a novel machine learning approach to use a stack ensemble of Random forest, Gradient boost with XGBoost, and Logistic regression as meta classifiers. To solve the problem related to data imbalance, the ADASYN oversampling method is used to increase the number of samples of the minority class. The results are then measured with Recall, FNR, ROC AUC in order to see that the model works effectively to detect theft with good levels of TPR without crossing FDR and FNDR levels. This work provides a useful application to various utility companies designed to identify fluctuations in energy consumption, protect power distribution networks, and minimize energy losses.

Ask AI

Helpful

Bookmark

View Full Paper