Electricity theft presents significant challenges globally, with traditional detection methods often lagging behind sophisticated techniques. A misuse of authority can have several detrimental effects. These include rising energy consumption, strain on the infrastructure that supplies it, falling power company profits, and risks to public safety such as electrical shocks and fires caused by using electricity. The proposed model used an ensemble method involving voting and stacking methods to train a challenging imbalanced dataset of electricity theft. The ensemble method used logistic regression and random forest models with ADASYN (adaptive synthetic sampling) to achieve the best results. The dataset comprised 1034 customer records (2014–2016), exhibiting marked class imbalance that was corrected to equal class representation using ADASYN. On the ADASYN-balanced data, the stacking model (logistic regression + random forest) delivered class-wise precision/recall/F1 of 0.95/0.94/0.94 for “theft” and 0.94/0.95/0.94 for “non-theft,” with overall accuracy of 0.94. Discrimination performance was strong (ROC-AUC ≈ 0.94), surpassing the voting ensemble (AUC ≈ 0.93) when both were trained on balanced data. Confusion-matrix and metric profiles further show stacking on balanced data outperformed all imbalanced settings and the voting baseline. Experimental results showed that stacking with the combination of logistic regression and random forest achieved the best results from benchmarks of 94% accuracy, recall, and F1-score. These findings indicate a robust, lightweight approach for electricity theft detection that improves minority-class detection without sacrificing overall accuracy.
Saqib et al. (Tue,) studied this question.