Enhancing Urban Sustainability through AI-Driven Energy Management: Predicting Consumption Patterns and Minimizing Carbon Footprints

With the expansion of urban areas and escalation in climate change, strengthening sustainability through effective energy handling has become inevitable. This research work identifies the role of Artificial Intelligence (AI) in enhancing urban sustainability by forecasting energy usage patterns and reducing carbon footprints. various AI models are examined, such as Artificial Neural Networks (ANN), Random Forests, Decision Trees, Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), focusing on their application in predicting energy utilization and augmenting allocation of resources. This paper highlights how the AI driven techniques contribute to more precise forecast and energy efficient consumption by examining latest developments and performances in various urban environments. Moreover, potential strategies for lowering the carbon footprints and improving the energy efficiency will be discussed. This research work lines up with SDG 11: Sustainable Cities and Communities, the United Nations Sustainable Development Goal, with the main objective to create a resilient, resource-efficient urban environments.