ChatEV: Predicting electric vehicle charging demand as natural language processing

IF 7.3 1区 工程技术 Q1 ENVIRONMENTAL STUDIES
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引用次数: 0

Abstract

The increasing popularity of electric vehicles (EVs) in recent times has introduced considerable load conditions for urban power grids and transportation systems, which highlights the importance of accurately predicting charging demand to enhance charging efficiency. However, current forecasting methods still face challenges in effectively aligning diverse data and generating accurate predictions that can be applied to unseen scenarios. To overcome the challenges, this work introduces a novel perspective: employing large language models (LLMs) as EV charging demand predictors. First, we reformulate the prediction task into a text-to-text format, enabling seamless and effective alignment of various features within a unified language semantic space. Subsequently, we fine-tune a LLM using a meta-learning framework to adapt it specifically for EV charging prediction. Through comprehensive evaluations, it has been demonstrated that the proposed model, ChatEV, achieves outstanding performance in EV charging demand forecasting, particularly in scenarios with limited data.
ChatEV: 通过自然语言处理预测电动汽车充电需求
近来,电动汽车(EV)的日益普及为城市电网和交通系统带来了巨大的负载条件,这凸显了准确预测充电需求以提高充电效率的重要性。然而,目前的预测方法在有效整合各种数据并生成可应用于未知场景的准确预测方面仍面临挑战。为了克服这些挑战,这项工作引入了一个新的视角:采用大型语言模型(LLM)作为电动汽车充电需求预测工具。首先,我们将预测任务重新表述为文本到文本格式,从而在统一的语言语义空间内无缝、有效地调整各种特征。随后,我们利用元学习框架对 LLM 进行微调,使其专门用于电动汽车充电预测。通过综合评估,证明了所提出的模型 ChatEV 在电动汽车充电需求预测方面,尤其是在数据有限的情况下,取得了出色的性能。
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来源期刊
CiteScore
14.40
自引率
9.20%
发文量
314
审稿时长
39 days
期刊介绍: Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution. We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.
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