AI-Driven Urban IoT Framework for Smart Solar Energy Forecasting and Optimization in Smart Cities

Abstract

Rapid urbanization has increased the demand for efficient and sustainable energy management in smart cities. Traditional urban energy systems are largely reactive and struggle to handle the uncertainty of renewable energy generation, especially from solar power. To address this challenge, this paper proposes an AI-driven Urban IoT framework for smart solar energy forecasting and optimization. The proposed system integrates IoT devices such as smart meters, environmental sensors, and solar panels to collect real-time urban energy data. A Long ShortTerm Memory (LSTM) deep learning model is used to predict short-term solar energy generation based on historical and environmental parameters. The predicted output is then used by an intelligent decision-making module to optimize energy allocation among solar supply, battery storage, and the conventional power grid. The framework is designed with a multi-layer architecture comprising sensing, edge processing, cloud analytics, AI intelligence, and application layers, enabling scalable and real-time operation. Experimental evaluation using simulated and real-time datasets shows improved prediction accuracy, reduced grid dependency by approximately 28 percent, faster response time of around 180 ms, and high system reliability. The results indicate that the proposed framework can enhance renewable energy utilization, improve operational efficiency, and support sustainable smart city energy management.