Volume 3 Issue 1 | 2026 | View PDF
Paper Id:IJMSM-V3I1P123
doi: 10.71141/30485037/V3I1P123

Improving Performance of Electricity Theft Detection Using Combined Machine Learning Models with Real Applications in Vietnam

Ong Luong Tien Huy, Nguyen Quang Tung, Nguyen Hoang Nam Anh, Nguyen Quang Minh, Ngo Le Duc Anh, Vo Minh Khoi, Vu Xuan Manh

Citation:
Ong Luong Tien Huy, Nguyen Quang Tung, Nguyen Hoang Nam Anh, Nguyen Quang Minh, Ngo Le Duc Anh, Vo Minh Khoi, Vu Xuan Manh, "Improving Performance of Electricity Theft Detection Using Combined Machine Learning Models with Real Applications in Vietnam" International Journal of Multidisciplinary on Science and Management, Vol. 3, No. 1, pp. 254-260, 2026.

Abstract:
The detection of electricity has been well researched in recent years. The topic focuses on the This study focuses on electricity theft, classified as non-technical losses, which adversely affects both power distribution companies and consumers and may lead to serious consequences such as fires and power outages. The research aims at coming up with an effective machine learning-based solution to the detection of electricity theft in smart grid environments. The dataset is made up of records of electricity consumption of 34,823 customers served by Vietnam Electricity Corporation. The suggested methodology will involve data preprocessing (handling missing values and normalization), taking out features of consumption patterns, and data balancing due to the difference between the number of normal users and electricity theft cases. The experimental findings prove that the suggested method performs very well in terms of detection and the accuracy reaching up to 99%.

Keywords: Electrictity Theft Detection, Machine Learning, Data Processing, Feature Extraction.

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