Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models

TL;DR

This paper introduces EV-LLM, a novel model combining graph neural networks and large language models to improve spatiotemporal forecasting of EV charging loads, leveraging multimodal data for better grid management.

Contribution

The paper presents EV-LLM, a new approach integrating GCNs and LLMs for enhanced EV load prediction using diverse data sources, which outperforms traditional methods.

Findings

EV-LLM achieves higher forecasting accuracy than traditional deep learning models.

The model effectively incorporates multimodal data including weather and textual descriptions.

Demonstrates potential to optimize grid scheduling and vehicle-to-grid interactions.

Abstract

The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively harnessing the spatiotemporal correlations among EV charging stations to improve forecasting accuracy is complex. To tackle these challenges, we propose EV-LLM for EV charging loads based on LLMs in this paper. EV-LLM integrates the strengths of Graph Convolutional Networks (GCNs) in spatiotemporal feature extraction with the generalization capabilities of fine-tuned generative LLMs. Also, EV-LLM enables effective data mining and feature extraction across multimodal and multidimensional datasets, incorporating historical charging data, weather information, and relevant textual descriptions to enhance forecasting accuracy for multiple charging stations. We validate the effectiveness of EV-LLM by using charging data from 10 stations in California, demonstrating its superiority over the other traditional deep learning methods and potential to optimize load grid scheduling and support vehicle-to-grid interactions.