- Title
- Performance analysis of machine learning classifiers for non-technical loss detection
- Creator
- Ghori, Khawaja; Imran, Muhammad; Nawaz, Asad; Abbasi, Rabeeh; Ullah, Ata; Szathmary, Laszlo
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198894
- Identifier
- vital:19108
- Identifier
-
https://doi.org/10.1007/s12652-019-01649-9
- Identifier
- ISSN:1868-5137 (ISSN)
- Abstract
- Power companies are responsible for producing and transferring the required amount of electricity from grid stations to individual households. Many countries suffer huge losses in billions of dollars due to non-technical loss (NTL) in power supply companies. To deal with NTL, many machine learning classifiers have been employed in recent time. However, few has been studied about the performance evaluation metrics that are used in NTL detection to evaluate how good or bad the classifier is in predicting the non-technical loss. This paper first uses three classifiers: random forest, K-nearest neighbors and linear support vector machine to predict the occurrence of NTL in a real dataset of an electric supply company containing approximately 80,000 monthly consumption records. Then, it computes 14 performance evaluation metrics across the three classifiers and identify the key scientific relationships between them. These relationships provide insights into deciding which classifier can be more useful under given scenarios for NTL detection. This work can be proved to be a baseline not only for the NTL detection in power industry but also for the selection of appropriate performance evaluation metrics for NTL detection. © 2020, The Author(s).
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Journal of Ambient Intelligence and Humanized Computing Vol. 14, no. 11 (2023), p. 15327-15342
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2020, The Author(s)
- Rights
- Open Access
- Subject
- 46 Information and computing sciences
- Full Text
- Reviewed
- Funder
- Open access funding provided by University of Debrecen (DE). We are thankful to the electric supply company in Pakistan for giving us a copy of the dataset containing monthly consumption of their customers for the prediction of NTL. The work/publication is supported by the EFOP-3.6.1-16-2016-00022 project.The project is co-financed by the European Union and the European Social Fund. Imran’s work is supported by the Deanship of Scientific Research through research group project number RG-1435-051.
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