AI-ENHANCED ENERGY MANAGEMENT FOR OPTIMIZING RENEWABLE ENERGY INTEGRATION IN SMART CITIES

This paper proposes an artificial intelligence-based energy management system implemented on the Internet of Things in smart cities to optimize the amount of renewable energy used in a smart city, reduce costs, and improve stability in the grid. This system combines machine learning methods (LSTM and SVM), Mixed-Integer Linear Programming (MILP) optimization, and reinforcement learning (RL) to predict energy generation and storage, as well as for balancing load in the grid. We validated the results with real data and proved that our model reduced energy costs by 12%, increased the use of renewable energy by 10%, and improved energy balance by 2.3%. Also, grid stability was enhanced by a 66% decrease in failures and 50% in outage periods. Although the system demonstrated potentially successful outcomes, it relies on data quality and computational power. Future efforts will prioritize improving prediction accuracy using up-to-date weather data and expanding the system to encompass larger urban areas. Building systems have an excellent scope for reliable, energy-efficient, and sustainable energy management in smart cities, enabling innovative and eco-friendly urban infrastructure.