Optimizing Energy Consumption in Smart Cities Using Machine Learning

Abstract

The rapid urbanization of smart cities has led to an increased demand for sustainable energy solutions. This paper explores the integration of machine learning (ML) techniques to optimize energy consumption in smart cities. By leveraging predictive analytics and real-time monitoring, ML models can enhance energy efficiency, minimize waste, and improve demand-side management. We evaluate various ML algorithms, including reinforcement learning and deep neural networks, to forecast energy demand and optimize grid distribution. Case studies from major cities implementing ML-based energy solutions are analyzed to assess their impact on sustainability. Our findings highlight the potential of AI-driven frameworks in reducing carbon footprints and enhancing urban sustainability.