A Comprehensive Machine Learning Framework for Predicting the Energy and Economic Impact of Electric City Buses

Abstract

The research work currently attempts to reduce the carbon emissions and make energy-efficient urban public transportation with electric buses. This research sets up a big data analytics framework with machine learning to forecast and optimize the energy consumption of electric city buses. Such system offers accurate prediction of energy economy by utilizing real-time large-scale telematics and operational information processed via batch and stream through Apache Spark. As per the objective of fast-paced transit environment subjected to continuous disturbances by traffic, weather, and vehicle load, the scalability of the framework serves as a crucial capacity for distributed computation and in-memory processing. Energy consumption can be viewed in a holistic manner charged with heterogeneous data sources. Such predictive insights enable transit agencies to undertake proactive energy strategies, model optimization on routes, and introduction of batteries that last longer into lower operational costs with reduced environmental impact. Future work will be targeted towards real-time integrated streaming tools such as Apache Kafka and Flink and deploy advanced models like LSTM and Reinforcement Learning while developing visual analytics and cloud scale. The research will explore how NLP can be subjected to use for unstructured data analysis, for instance through driver logs and maintenance reports. From intelligent transport systems, this framework is considered a great major step and indeed becomes a crucial building block towards the vision of smart energy-efficient cities.