Improving Smart City Energy Efficiency with the Use of Artificial Intelligence and Machine Learning Methods

Abstract

One response to urbanization's fast pace and the pressing need for sustainable development has been the emergence of "smart cities," which rely heavily on technological means of efficient resource management. A smart city can improve its energy efficiency by utilizing AI and ML techniques. Artificial intelligence (AI) powered solutions can help smart cities' energy systems distribute power more efficiently, reduce waste, and achieve real-time demand and supply balance. Predictive analytics and deep learning are two examples of machine learning algorithms that may properly estimate a building's future energy use and provide creative solutions to reduce that consumption. Among the most significant AI and ML techniques employed in energy optimization are smart grids, energy systems enabled by the Internet of Things, and adaptive algorithms for load balancing. It goes on to mention the challenges of implementing AI in smart city systems, including concerns over privacy and data security as well as the requirement for extremely strong computing resources. Smart communities can achieve their sustainability goals by keeping an eye on future trends and considering how AI and ML could impact energy management.