Anomaly Detection and Optimization in IoT Connected Smart Grids
Anomaly Detection and Optimization in IoT Connected Smart Grids
Nirup Kumar Reddy Pothireddy
TLDR
This research proposes an AI-based anomaly detection system for real-time identification of energy theft, voltage fluctuation, and device malfunction, and a recommendation engine integrates load balancing, predictive maintenance, and energy distribution strategies, which enhances grid reliability, resilience, and sustainability.
Abstract
IoT-enabled smart grids use data from smart meters, transformers, and power-distribution units to optimize energy efficiency and seamless power management. However, when there are sensor faults, unauthorized energy consumption, or sudden fluctuations in demand, operations are compromised with inefficient or potential failures. This research proposes an AI-based anomaly detection system for real-time identification of energy theft, voltage fluctuation, and device malfunction. Secondly, a recommendation engine integrates load balancing, predictive maintenance, and energy distribution strategies, which enhances grid reliability, resilience, and sustainability.