Intellectual Property India Publishes Patent Application for Ai-Driven Iot And Cloud-Based System For Intelligent Electrical Theft Prediction And Real-Time Energy Monitoring

MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641088362 A) filed by Siva Yenikepalli; Sumathi G; Sivaprabha T; B Sri Vijaya Rekha; Jothippriya N; Polu Veeraraghava Reddy; Dr. Pendurthy Anthony Sunny Dayal; R Prabhu; Prajna K B; Dr. Manoj Dattatray Nikam; Lovdeep Grover; and S. Devilavanya on July 20, 2026, for Ai-Driven Iot And Cloud-Based System For Intelligent Electrical Theft Prediction And Real-Time Energy Monitoring.

Inventors include Siva Yenikepalli; Sumathi G; Sivaprabha T; B Sri Vijaya Rekha; Jothippriya N; Polu Veeraraghava Reddy; Dr. Pendurthy Anthony Sunny Dayal; R Prabhu; Prajna K B; Dr. Manoj Dattatray Nikam; Lovdeep Grover; and S. Devilavanya.

The application for the patent was published on July 31, 2026, under issue no. 31/2026.

Abstract: AI-Driven IoT and Cloud-Based System for Intelligent Electrical Theft Prediction and Real-Time Energy Monitoring is the proposed invention. The proposed invention discloses an AI-driven IoT and cloud-based system for intelligent electrical theft prediction and real-time energy monitoring that leverages an Informer-based deep learning model for accurate long-term electricity consumption forecasting and early theft detection. The system integrates IoT-enabled smart meters, edge computing devices, secure cloud infrastructure, and advanced analytics to continuously collect and process electrical parameters, including voltage, current, power consumption, power factor, frequency, harmonic distortion, and transformer loading. After preprocessing at the edge, the data are transmitted to the cloud, where the Informer model forecasts expected energy consumption and compares it with real-time meter readings to identify anomalies associated with electricity theft, meter tampering, unauthorized connections, or abnormal usage patterns. An intelligent anomaly assessment engine assigns theft risk scores, generates real-time alerts, and provides interactive monitoring dashboards for utility operators. The framework continuously updates its predictive model using newly acquired operational data, enabling adaptive learning and improved detection accuracy. The proposed invention enhances billing transparency, reduces non-technical losses, improves grid reliability, and offers a scalable, intelligent solution for next-generation smart energy distribution networks.

Disclaimer: Curated by HT Syndication.