کنفرانس مهندسی برق ایران

صفحه اصلی / سی و چهارمین کنفرانس بین المللی مهندسی برق

A Data-Driven Framework for Forecasting EV Charging Infrastructure Using Large Language Models

نویسندگان :

Mohammad Reza Mansouri¹ Reza Gharibi² Ali Darvishi³ Behnam Ranjbar⁴ Rahman Dashti⁵

1- Persian Gulf University 2- Persian Gulf University 3- Persian Gulf University 4- University of Tehran 5- Persian Gulf University

کلمات کلیدی :

Electric Vehicle Charging Stations (EVCS)،Large Language Model (LLM)،Location Prediction،Machine Learning،K-Nearest Neighbors (KNN)،Population Density،Points of Interest (POI)

چکیده :

With increasing electric vehicle (EV) adoption, optimal charging station (EVCS) installation is vital for urban planning. Determining suitable locations remains a significant challenge. Previous Dubai studies used traditional machine learning like K-Nearest Neighbors (KNN), Logistic Regression, Neural Networks, and Support Vector Machines. Trained on 80% of the dataset, these achieved up to 89% KNN accuracy, using features like geographic coordinates, population density, Points of Interest (POI), and security cameras. This paper introduces a novel Large Language Model (LLM) approach. Structured data is converted to text; Llama 3 8B is fine-tuned via QLoRA for EVCS classification. Results show substantial accuracy and data efficiency: 98% (60% data) and 97% (50% data). This contrasts significantly with the 89% accuracy from 80% training data in prior methods. Fine-tuned LLMs prove a powerful, highly dataefficient tool for optimizing EVCS distribution and advancing smart city infrastructure.