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

Khawaja Moyeezullah Ghori*, Rabeeh Ayaz Abbasi, Muhammad Awais, Muhammad Imran, Ata Ullah, Laszlo Szathmary

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
65 Downloads (Pure)

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.
Original languageEnglish
Article number8943419
Pages (from-to)16033-16048
Number of pages16
JournalIEEE Access
Volume8
Early online date26 Dec 2019
DOIs
Publication statusPublished - 27 Jan 2020

Keywords

  • boosting
  • classification algorithms
  • Data mining
  • machine learning
  • supervised learning

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