The escalating demand for energy in rapidly urbanizing regions presents significant challenges to conventional electrical infrastructure. In the context of smart cities, there is an urgent need for intelligent systems capable of monitoring, analyzing, and optimizing energy usage to ensure sustainability and reduce environmental impact. This paper proposes the design and implementation of an AI-powered, IoT-based Smart Energy Management System (SEMS) that leverages real-time data from distributed sensors and smart meters. By integrating machine learning algorithms, edge computing, and secure communication protocols, the proposed system can predict energy consumption patterns, optimize load distribution, and facilitate seamless integration of renewable energy sources such as solar and wind. The system architecture comprises IoT-enabled sensors, a local edge computing unit for real-time analytics, and a cloud-based platform for long-term data storage and system-wide optimization. Advanced AI models are trained on historical and real-time data to provide demand forecasting, fault detection, and energy efficiency recommendations. Experimental results from a prototype deployed in a controlled urban microgrid demonstrate significant improvements in energy efficiency, reduced peak load, and better resource utilization compared to traditional energy management systems. In this work highlights the potential of combining electrical, electronics, and computing technologies to develop scalable, adaptive, and intelligent solutions for the next generation of sustainable urban infrastructure.
Vallem Ranadheer Reddy (Tue,) studied this question.
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