Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Abstract

With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

CatBoost for Big Data: an Interdisciplinary Review

“…Interestingly, we find a description of a process similar to IFS in [35], but it is not referred to as such. However, in [35] the authors report finding 14 useful features after applying feature selection, whereas in [36] the authors report finding 9 useful features. The results in [35,Tab.…”

“…CatBoost has highest precision and F-measure, ANN has 0.003 higher recall Reference: [35] Title: Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing Description:…”

“…Ghori et al's study [36] was published as a part of the 6th International Conference on Data Science and Machine Learning Applications. Apparently they were expanding this work for "Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection" [35] that appeared in the Journal "IEEE Access" around the same time that [36] was published. Both studies have the same authors, and the data descriptions in both studies indicate they use similar data.…”

“…Both studies have the same authors, and the data descriptions in both studies indicate they use similar data. In [35], the authors augment the collection of classifiers they use to include an Artificial Neural Network (ANN). Interestingly, we find a description of a process similar to IFS in [35], but it is not referred to as such.…”

“…In [35], the authors augment the collection of classifiers they use to include an Artificial Neural Network (ANN). Interestingly, we find a description of a process similar to IFS in [35], but it is not referred to as such. However, in [35] the authors report finding 14 useful features after applying feature selection, whereas in [36] the authors report finding 9 useful features.…”