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Ghori, Khawaja Moyeezullah, Abbasi, Rabeeh Ayaz, Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Imran, Muhammad, Ullah, Ata and Szathmary, Laszlo
(2020)
Performance analysis of different types of machine learning classifiers for non-technical loss detection.
IEEE Access, 8.
pp. 16033-16048.
ISSN 2169-3536
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This work was supported in part by the construction under Grant EFOP-3.6.3-VEKOP-16-2017-00002, in part by the European Union, and in part by the by the European Social Fund. |
| Uncontrolled Keywords: | data mining,boosting,classification algorithms,machine learning,supervised learning,computer science(all),materials science(all),engineering(all) ,/dk/atira/pure/subjectarea/asjc/1700 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Oct 2023 00:45 |
| Last Modified: | 10 Dec 2024 01:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93314 |
| DOI: | 10.1109/access.2019.2962510 |
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