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.

中文翻译：

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机器学习算法在管理异常消费者行为和增强公共服务中的应用。

由于非技术损失 (NTL)，消费者越轨行为每年使全球公用事业公司损失 960 亿美元。NTL 通过使线路和变压器过载来影响电力系统的运行，导致电压不平衡，从而影响服务。它们还会影响诚实客户支付的电价。传统电表占印度总电表的 98%。本文认为，虽然传统仪表在检查消费者异常行为方面存在局限性，但可以使用基于 ML 的算法解决这个问题。这些算法可以以合理的确定性预测疑似盗窃案件，从而使分销公司能够节省资金并以合理的成本为诚实的客户提供一致和可靠的服务。