Abstract
Non-technical losses (NTLs) have been a major concern for power distribution companies (PDCs). Billions of dollars are lost each year due to fraud in billing, metering, and illegal consumer activities. Various studies have explored different methodologies for efficiently identifying fraudster consumers. This study proposes a new approach for NTL detection in PDCs by using the ensemble bagged tree (EBT) algorithm. The bagged tree is an ensemble of many decision trees which considerably improves the classification performance of many individual decision trees by combining their predictions to reach a final decision. This approach relies on consumer energy usage data to identify any abnormality in consumption which could be associated with NTL behavior. The key motive of the current study is to provide assistance to the Multan Electric Power Company (MEPCO) in Punjab, Pakistan for its campaign against energy stealers. The model developed in this study generates the list of suspicious consumers with irregularities in consumption data to be further examined on-site. The accuracy of the EBT algorithm for NTL detection is found to be 93.1%, which is considerably higher compared to conventional techniques such as support vector machine (SVM), k-th nearest neighbor (KNN), decision trees (DT), and random forest (RF) algorithm.
Highlights
Electrical energy losses in any transmission and distribution system include both technical and non-technical losses
Unlike the previous research works on Non-technical losses (NTLs) detection system [21,22,46], this research work has considered all the key evaluation measures in order to have a fair comparison between different classifiers
This study has offered a new approach for NTL detection in power distribution companies (PDCs) using one of the most efficient classifying algorithms called ensemble bagged tree (EBT) algorithm
Summary
Electrical energy losses in any transmission and distribution system include both technical and non-technical losses. This research work explores a classification approach based on EBT algorithm and proposes the same to support the PDCs by effectively detecting fraudster consumers. The proposed NTL scheme has achieved the maximum detection rate and minimum false positives (FP) as compared to the conventional methods on Multan Electric Power Company (MEPCO) real-time dataset and can be considered as the first-ever study of electricity fraud detection in Pakistan PDCs. for the very first time in literature, the EBT algorithm has been explored for NTL detection which has attained the higher detection rate than that of its counterpart machine learning algorithms on conventional energy meter’s dataset. The proposed EBT classification scheme utilizes the consumer’s electricity consumption data from MEPCO Multan, Pakistan, to classify the honest and fraudster consumer.
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