AI-Driven Smart Energy Management Systems for Optimized Renewable Energy Utilization in Urban Smart Grids

Integration of Smart Energy Management Systems (SEMS) in urban grids has become a necessity to overcome the challenges of energy demand fluctuations, renewable energy intermittency, and grid stability. This paper introduces an AI-driven SEMS framework that optimizes the integration of renewable energy sources (RES), including solar and wind, into urban grids, reducing energy wastage and operational costs by a significant margin. The proposed system utilizes advanced machine learning algorithms, such as long short-term memory (LSTM) and reinforcement learning, to predict energy demand with an accuracy of 93.5%, optimizing energy distribution with a 17% reduction in peak load demand. Experimental verification was carried out in a real urban environment, consuming 4.8 GWh of total energy per year, showing that the AI-driven system increased overall energy efficiency by 25% as compared to rule-based traditional energy management systems. Moreover, the cost analysis also shows the savings of 15.3% in the operation costs, thus making the estimated yearly saving $1.05 million for the studied region. The system was able to balance energy supply and demand through the use of excess solar generation, achieving a renewable energy utilization rate of 78.9%, while minimizing grid dependency by 30.5%. Demand-side response strategies resulted in a 20.1% improvement in load factor, which ensured a more stable grid operation and reduced carbon emissions by 27.8%, contributing to the city's sustainability targets. The proposed novel AI-driven SEMS represents a scalable solution adaptable to a wide range of urban infrastructures, thus furthering the path to the transition towards sustainable energy systems in terms of improving resilience, reliability, and economic feasibility. Results from this work are critical inputs for the implementation of practical AI-enhanced energy management strategies while underscoring the central role intelligent optimization plays in accomplishing energy transition goals.