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SMART ENERGY FORECASTING FOR ELECTRIC CITY BUSES USING MACHINE LEARNING
India is rapidly transitioning toward the electrification of public transportation—particularly electric buses—to reduce carbon emissions and dependency on fossil fuels. As the country's energy demands continue to grow, electric buses have emerged as a key solution for sustainable urban transport. By 2022, India had more than 4,000 electric buses in operation, a significant increase from just a few hundred in 2017. This growth reflects the nation's strong commitment to reducing pollution and improving energy efficiency. To further enhance operational performance, a data-driven approach using machine learning is being applied to predict energy consumption and optimize economic performance in electric city buses. This approach aims to improve the overall efficiency and sustainability of urban public transportation systems. Traditionally, electric bus management relied on static route planning, manual scheduling, and historical data, making the system reactive rather than proactive. Maintenance and energy optimization were often performed only after issues occurred, leading to inefficiencies and increased operational costs. These systems lacked predictive analysis, resulting in inefficient energy use, suboptimal route planning, and unpredictable expenses. As urban centers face rising energy demands and environmental pressures, there is a clear need for intelligent, dynamic fleet management. A machine learning-based system can analyze real-time data from electric buses—including battery levels, route patterns, and weather conditions—to accurately predict energy consumption and operational costs. This enables dynamic route optimization, predictive maintenance, and load balancing, significantly reducing energy waste and operational costs while enhancing the efficiency of public transport systems. Additionally, AI-driven models will provide real-time insights, allowing fleet operators to make proactive decisions that improve overall performance and reliability.
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