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Application of Machine Learning Algorithm in Managing Deviant Consumer Behaviors and Enhancing Public Service.

Application of Machine Learning Algorithm in Managing Deviant Consumer Behaviors and Enhancing Public Service.

Shantanu Dubey, Prashant Salwan, Nitin Kumar Agarwal
Copyright: © 2022 |Volume: 30 |Issue: 5 |Pages: 24
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668445792|DOI: 10.4018/JGIM.292064
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MLA

Dubey, Shantanu, et al. "Application of Machine Learning Algorithm in Managing Deviant Consumer Behaviors and Enhancing Public Service." JGIM vol.30, no.5 2022: pp.1-24. http://doi.org/10.4018/JGIM.292064

APA

Dubey, S., Salwan, P., & Agarwal, N. K. (2022). Application of Machine Learning Algorithm in Managing Deviant Consumer Behaviors and Enhancing Public Service. Journal of Global Information Management (JGIM), 30(5), 1-24. http://doi.org/10.4018/JGIM.292064

Chicago

Dubey, Shantanu, Prashant Salwan, and Nitin Kumar Agarwal. "Application of Machine Learning Algorithm in Managing Deviant Consumer Behaviors and Enhancing Public Service.," Journal of Global Information Management (JGIM) 30, no.5: 1-24. http://doi.org/10.4018/JGIM.292064

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Abstract

Consumer-deviant behavior costs global utility firms USD 96 billion yearly, attributable to Non-Technical Losses (NTLs). NTLs affect the operations of power systems by overloading lines and transformers, resulting in voltage imbalances and, thereby, impacting services. They also impact the electricity price paid by the honest customers. Traditional meters constitute 98 % of the total electricity meters in India. This paper argues that while traditional meters have their limitation in checking consumer-deviant behavior, this issue can be resolved with ML-based algorithms. These algorithms can predict suspected cases of theft with reasonable certainty, thereby enabling distribution companies to save money and provide consistent and dependable services to honest customers at reasonable costs. The key learning from this paper is that even if data is noisy, it is possible to create a Machine Learning Model to detect NTL with 80 percentage plus accuracy.