Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks

Fuente: arXiv
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Autores principales: Orfanoudakis, Stavros, Palensky, Peter, Vergara, Pedro P.
Formato: Preprint
Publicado: 2025
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author Orfanoudakis, Stavros
Palensky, Peter
Vergara, Pedro P.
author_facet Orfanoudakis, Stavros
Palensky, Peter
Vergara, Pedro P.
contents Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions.
format Preprint
id arxiv_https___arxiv_org_abs_2502_03067
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks
Orfanoudakis, Stavros
Palensky, Peter
Vergara, Pedro P.
Systems and Control
Machine Learning
Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions.
title Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks
topic Systems and Control
Machine Learning
url https://arxiv.org/abs/2502.03067