A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies

Sufian A. Badawi, Djamel Guessoum, Isam Elbadawi and Ameera Albadawi
Additional contact information
Sufian A. Badawi: Department of Computing, School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Djamel Guessoum: Electrical Engineering Department, Ecole de Technologie Superieure, Montreal, QC H3C 1K3, Canada
Isam Elbadawi: Industrial Engineering Department, College of Engineering, University of Hail, Ha’il 81481, Saudi Arabia
Ameera Albadawi: Department of Analytics in the Digital Era, College of Business and Economics, United Arab Emirates University, Al Ain 15551, United Arab Emirates

Mathematics, 2022, vol. 10, issue 11, 1-16

Abstract: Several approaches have been proposed to detect any malicious manipulation caused by electricity fraudsters. Some of the significant approaches are Machine Learning algorithms and data-based methods that have shown advantages compared to the traditional methods, and they are becoming predominant in recent years. In this study, a novel method is introduced to detect the fraudulent NTL loss in the smart grids in a two-stage detection process. In the first stage, the time-series readings are enriched by adding a new set of extracted features from the detection of sudden Jump patterns in the electricity consumption and the Autoregressive Integrated moving average (ARIMA). In the second stage, the distributed random forest (DRF) generates the learned model. The proposed model is applied to the public SGCC dataset, and the approach results have reported 98% accuracy and F1-score. Such results outperform the other recently reported state-of-the-art methods for NTL detection that are applied to the same SGCC dataset.

Keywords: non-technical loss; electricity smart meters fraud; time series; random forest (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/11/1878/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/11/1878/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:11:p:1878-:d:828024

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().