Urban IoT System for AI-Driven Smart Solar Energy Management in Smart Cities

Modern cities have been facing lots of difficulties in terms of sustainability, operational efficiency, environmental degradation, and grid reliability as a result of rapid urbanization which has greatly increased the energy consumption in the cities. The existing systems of urban energy management are mostly reactive and not proactive and predictive and therefore ineffective in their use of renewable energy and overdependence on the conventional power grids. The paper will present a detailed Urban IoT-based framework that will be used to transform the traditional cities into sustainable smart cities by means of AI-enhanced solar energy management. The framework combines IoT-enabled solar panels, weather sensors, intelligent meters, and Long Short-Term Memory (LSTM) based deep learning models to predict solar power under different environmental conditions. An organized process with realtime data collection, smart pre-processing, multi-horizon forecasting, energy optimization, anomaly detection, visualization and feedback learning makes it possible to utilize energy optimally. The system was tested during a Nagpur Smart City pilot implementation which showed a 28% decrease in grid reliance and a substantial efficiency improvement in renewable energy usage.