Artificial Intelligence Approaches to Energy Efficiency in Next-Generation Smart Cities

Abstract

With urbanization taking place at a rapid pace in cities in India, developing smart cities has become a national priority, and Karnataka is leading the efforts. Implementing artificial Intelligence (AI) in city energy systems offers tremendous opportunities for improvement in efficiencies, emission mitigations, and urban living enhancements. This project will examine a variety of AI-based energy optimization approaches across the next generation of smart cities in Karnataka.

Purpose: This study seeks to assess the use of Artificial Intelligence (AI) to improve energy efficiency in the next-generation smart cities of Karnataka, specifically Bengaluru, Mysuru, Hubballi-Dharwad, and Mangaluru. It examines AI uses in smart grids, building energy management, renewable energy forecasting, sustainable mobility; and proposes a scalable AI framework for energy efficiency strategies that considers the climatic, infrastructural, and demographic characteristics of the state of Karnataka to foster sustainable cities.

Methodology: This study utilized a mixed-methods approach with primary data from agencies in Karnataka (2019–2023), and used secondary sources. The case studies of AI were conducted in Bengaluru, Mysuru, Hubballi-Dharwad, and Mangaluru, simulations with TensorFlow, Scikit-learn, and LSTM, explored energy savings, prediction accuracy, peak load reduction, and carbon emissions reduction.

Major Findings:The study found that AI systems are capable of reducing energy use in buildings by 15-30%, helped with grid balancing 25% better, and provided accurate solar forecasts that improved integrated renewables. AI traffic management helped to make cars 40% faster and reduce fuel use by 13%. The KAIEF framework provided a scalable and adaptable optimization framework.