Towards a Smarter Charging Infrastructure: Real-Time Availability Forecasting for EVs
Year: 2024
Type: conference-paper
Abstract: The electric vehicle (EV) market is experiencing substantial growth, and it is anticipated to play a major role as a replacement for fossil fuel-powered vehicles in transportation automation systems. Nevertheless, as a rule of thumb, EVs depend on electric charges, where appropriate usage, charging, and energy management are vital requirements. Examining the work done before gave us a reason and a basis for making a system that forecasts the real-time availability for EV charging stations that uses a scalable prediction engine built into a server-side software application that many people can use. The implementation process involved scraping data from various sources, creating datasets, and applying feature engineering to the data model. We then applied fundamental machine learning models to the pre-processed dataset, and subsequently, we implemented an ensemble model that combines the strengths of both Random Forest (RF) and Artificial Neural Network (ANN). This approach leverages the RF’s resilience to overfitting and its ability to handle diverse data while benefiting from the ANN’s capacity to capture complex non-linear relationships. The resulting ensemble model demonstrates significant improvements in precision, recall, and F1-score compared to individual models, making it a valuable contribution to the transportation sector.
Source: Procedia Computer Science
Authors: Meriem Manai, Bassem Sellami, Sadok Ben Yahia
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404801512