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
The charging behaviors of electric vehicle(EV)users exhibit high randomness and individual heterogeneity,with the key parameters such as the charging duration and charged energy levels displaying significant fluctuati...The charging behaviors of electric vehicle(EV)users exhibit high randomness and individual heterogeneity,with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations.Compared with EV cluster-layer prediction,predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables(e.g.,weather and holidays)and prediction accuracy,thereby imposing higher robustness requirements on prediction algorithms.An individual-user EV charging demand prediction method that in-tegrates multisource data with a dual-layer clustering approach and a light gradient boosting machine(LightGBM)is proposed in this study to address these technical challenges.First,a multisource dataset that incorporates user charging behavior data and exogenous variables(meteorological factors and date types)is constructed.A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed,thereby establishing a classi-fication feature space that characterizes different charging types and user groups.A predictive model is subse-quently developed using the LightGBM algorithm,which directly incorporates classification features as its inputs,effectively mitigating the information loss associated with the traditional categorical variable encoding process.Finally,employing EV users from a typical residential community in northern China as an empirical case,comparative experiments are performed to validate the proposed method,demonstrating its effectiveness at improving prediction accuracy.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.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB2403900.
文摘The charging behaviors of electric vehicle(EV)users exhibit high randomness and individual heterogeneity,with the key parameters such as the charging duration and charged energy levels displaying significant fluctuations.Compared with EV cluster-layer prediction,predicting the charging demands of individual users requires not only the analysis of more complex charging behaviors but also the establishment of a coupling model between exogenous variables(e.g.,weather and holidays)and prediction accuracy,thereby imposing higher robustness requirements on prediction algorithms.An individual-user EV charging demand prediction method that in-tegrates multisource data with a dual-layer clustering approach and a light gradient boosting machine(LightGBM)is proposed in this study to address these technical challenges.First,a multisource dataset that incorporates user charging behavior data and exogenous variables(meteorological factors and date types)is constructed.A dual-layer feature extraction mechanism consisting of data-layer clustering for charging type identification and user-layer clustering for user group classification is employed,thereby establishing a classi-fication feature space that characterizes different charging types and user groups.A predictive model is subse-quently developed using the LightGBM algorithm,which directly incorporates classification features as its inputs,effectively mitigating the information loss associated with the traditional categorical variable encoding process.Finally,employing EV users from a typical residential community in northern China as an empirical case,comparative experiments are performed to validate the proposed method,demonstrating its effectiveness at improving prediction accuracy.
