Harnessing predictive maintenance analytics to combat energy theft: A data-driven approach

Energy theft remains a critical challenge for utility companies worldwide, leading to significant financial losses, grid instability, and increased operational costs. Traditional detection methods often fall short in accurately identifying fraudulent activities due to their reactive nature and reliance on manual audits. This study explores the integration of predictive maintenance analytics (PMA) as a proactive, data-driven approach to combat energy theft. By leveraging machine learning algorithms, smart metering data, and advanced statistical modeling, PMA enhances anomaly detection, allowing utilities to identify irregular consumption patterns indicative of energy fraud. The proposed framework utilizes real-time data acquisition, predictive modeling, and automated anomaly detection to improve theft detection accuracy and minimize false positives. This approach enhances decision-making processes by integrating historical consumption trends, equipment performance metrics, and network load variations, thereby distinguishing between legitimate maintenance-related faults and fraudulent activities. The study further examines the role of Internet of Things (IoT) devices, cloud computing, and edge analytics in refining predictive capabilities, ensuring seamless scalability, and enabling real-time intervention. A case study on a utility provider demonstrates the effectiveness of PMA in detecting non-technical losses, reducing investigative costs, and improving overall grid efficiency. The findings indicate that adopting predictive maintenance analytics significantly enhances theft detection accuracy while optimizing asset performance. This research underscores the necessity of integrating artificial intelligence (AI)-powered analytics within energy infrastructures to fortify security, reduce losses, and establish a more resilient and sustainable power distribution network.