Abstract
Non-technical losses (NTL), which occur up to 40% of the total electric transmission and distribution power, create many challenges worldwide. These losses have a severe impact on distribution utilities and adversely affect the performance of electrical distribution networks. Furthermore, the depreciation of these NTL reduces the requirement of new power plants to fulfill the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper proposed a model for the detection of non-technical losses based on machine learning and feature engineering. Experimental results check the performance of the proposed model. These results clearly show that this proposed detection model has better accuracy, precision, recall, F1 score, and AUC score than other existing approaches.
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