Optimizing Energy Consumption in Smart Cities Using Reinforcement Learning Techniques

Abstract

Efficient energy management is crucial for sustainable smart cities. This research proposes a reinforcement learning-based model that dynamically optimizes energy distribution across urban infrastructures. By leveraging real-time data from sensors and smart grids, the system learns optimal policies to reduce energy waste and operational costs. Simulation results indicate substantial improvements in energy efficiency and system adaptability compared to conventional optimization methods.