AI-Driven Optimization for Renewable Energy Management in Smart Cities
Abstract
As urbanization expands, smart cities require efficient energy management to meet sustainability goals. This paper presents an AI-driven approach for optimizing renewable energy utilization in smart urban environments. By integrating predictive algorithms with real-time data from IoT sensors, our model enhances decision-making in energy distribution, storage, and consumption. Through case studies in wind and solar energy optimization, we demonstrate a reduction in energy wastage and increased grid stability, contributing to sustainable urban growth. Our findings highlight the potential of AI in maximizing the benefits of renewable energy sources within smart cities.
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