IOT Based Smart Energy Grid

The ever-growing demand for energy and the widespread integration of renewable sources have revealed the shortcomings of conventional electrical grids. Traditional centralized grids often lack real-time adaptability, leading to inefficient energy management, delayed fault detection, and challenges in accommodating fluctuations in power supply and demand. This project proposes a Smart Energy Grid framework that synergizes Internet of Things (IOT) technology with Artificial Intelligence (AI) and Machine Learning (ML) techniques. Embedded IOT devices are deployed throughout the grid to continuously monitor and collect data from generation, distribution, and consumption points. Advanced analytics powered by AI and ML process this data in real time to forecast energy usage, identify operational anomalies, and support predictive maintenance. Particular emphasis is placed on optimizing solar energy systems, addressing issues such as inefficient utilization , limited automation, and the absence of intelligence monitoring. By integrating sensing, cloud connectivity, and intelligence analytics, the system is designed to improve grid efficiency, reliability, and sustainability. This project demonstrates how a data-driven, adaptive approach can modernize energy management and enable smarter, more resilient grid operations.