With the rapid growth of the electric vehicle(EV)market,accurately predicting user charging behavior has become particularly important.This not only guides power distribution and charging station planning but is also ...With the rapid growth of the electric vehicle(EV)market,accurately predicting user charging behavior has become particularly important.This not only guides power distribution and charging station planning but is also crucial for improving user satisfaction and operational efficiency.This study aims to predict the charging behavior of EV users using Large Language Models(LLMs).Unlike traditional methods such as Long Short-Term Memory(LSTM)and XGBoost,or single-task prediction models,our proposed model,EVCharging-GPT,is the first to integrate the text generation capabilities of LLMs with a multi-task learning framework for EV user behavior prediction.We construct an EV user charging data processing flow and create a dataset of real scenarios for fine-tuning and testing the model.By carefully designing prompt templates,we transform the charging behavior prediction task into a text-to-text format,allowing the model to leverage its rich pre-trained knowledge base to make effective predictions.Additionally,we integrate temporal and static categorical features through natural language prompts and employ LoRA(Low-Rank Adaptation)technology to achieve efficient domain adaptation.To verify the effectiveness of the EVCharging-GPT model,we conduct extensive comparative experiments with various LLMs and traditional models.The results demonstrate the potential of the LLM-based approach for predicting user behavior in EVs and provide a solid foundation for future research and applications.展开更多
基金supported by the National Natural Science Foundation of China(No.62276026).
文摘With the rapid growth of the electric vehicle(EV)market,accurately predicting user charging behavior has become particularly important.This not only guides power distribution and charging station planning but is also crucial for improving user satisfaction and operational efficiency.This study aims to predict the charging behavior of EV users using Large Language Models(LLMs).Unlike traditional methods such as Long Short-Term Memory(LSTM)and XGBoost,or single-task prediction models,our proposed model,EVCharging-GPT,is the first to integrate the text generation capabilities of LLMs with a multi-task learning framework for EV user behavior prediction.We construct an EV user charging data processing flow and create a dataset of real scenarios for fine-tuning and testing the model.By carefully designing prompt templates,we transform the charging behavior prediction task into a text-to-text format,allowing the model to leverage its rich pre-trained knowledge base to make effective predictions.Additionally,we integrate temporal and static categorical features through natural language prompts and employ LoRA(Low-Rank Adaptation)technology to achieve efficient domain adaptation.To verify the effectiveness of the EVCharging-GPT model,we conduct extensive comparative experiments with various LLMs and traditional models.The results demonstrate the potential of the LLM-based approach for predicting user behavior in EVs and provide a solid foundation for future research and applications.
