Sohrab Alam, Majid Ashraf, Salman Alam

In smart grids the term non-technical losses impose challenges to perform classification, optimization, data analytics and regression analysis in almost all areas of real world research. The primary raw data suffers from an un-uniform distribution of one class over the other class in case of machine(ML) and Deep learning(DL). The data imbalanced, Overfiting, high False positive rate, handling of high dimensional data and generalization error impose chalanges for the industries and academia to detect the thieves of electricity efficiently. The aim of this article also to present comparative analysis of the approaches from the reference of data preprocessing, algorithms, models and hybrid paradigms for the coeval imbalance data analysis, overfitting, genersalization error, and high Fasle Possitive rate and the comparative study of different techniques and its application area.

- Sohrab Alam, sohrab.alamuet@gmail.com, Electrical Engineering Department, University of Engineering and Technology, Peshawar, Pakistan.
- Majid Ashraf, , Electrical Engineering Department, University of Engineering and Technology, Peshawar, Pakistan.
- Salman Alam, , Computer Science Department, COMSATS University, Abbottabad, Pakistan.

Sohrab Alam Majid Ashraf Salman Alam “A Systematic Review on Supervised Learning Techniques in Electricity Theft Detection” International Journal of Engineering Works Vol. 9 Issue 02 PP. 22-27 February 2022 https://doi.org/10.34259/ijew.22.9022227.

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