Scholarlink

<p>The rapid increase in the number of electric vehicles (EVs) has raised significant concerns regarding the stability of power grid operations and the effective management of charging infrastructure. In this review paper, we categorize EV charging demand forecasting into three perspectives: short-term, medium-term, and long-term. Subsequently, we compare and analyze the input characteristics essential for enhancing forecasting accuracy. Additionally, we comprehensively examine the features, advantages, and disadvantages of various methodologies employed in recent studies. Specifically, a systematic review was conducted encompassing contemporary research trends, including time-series models, deep learning (DL) approaches, growth-curve models, and scenario-based simulation models. Furthermore, we propose a model selection framework that comprehensively considers forecasting accuracy alongside realistic operational scenarios, such as the data-collection period and the optimal scale of EV charging stations. This framework employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a well-known multi-criteria decision making (MCDM) method technique, to evaluate the relative merits of different forecasting models. Ultimately, this study aims to provide researchers and practitioners engaged in EV charging demand forecasting with clear analytical criteria and practical guidelines for model selection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Review of Electric Vehicle Charging Demand Forecasting Models by Prediction Horizon: A Multi-Criteria Decision Analysis Approach

  • 期刊论文

  • 发表日期：

    2026年1月7日

查看全文
  • Minji Kim,
  • Won Young Park,
  • Hee Seung Moon,
  • Yun-Su Kim

摘要

The rapid increase in the number of electric vehicles (EVs) has raised significant concerns regarding the stability of power grid operations and the effective management of charging infrastructure. In this review paper, we categorize EV charging demand forecasting into three perspectives: short-term, medium-term, and long-term. Subsequently, we compare and analyze the input characteristics essential for enhancing forecasting accuracy. Additionally, we comprehensively examine the features, advantages, and disadvantages of various methodologies employed in recent studies. Specifically, a systematic review was conducted encompassing contemporary research trends, including time-series models, deep learning (DL) approaches, growth-curve models, and scenario-based simulation models. Furthermore, we propose a model selection framework that comprehensively considers forecasting accuracy alongside realistic operational scenarios, such as the data-collection period and the optimal scale of EV charging stations. This framework employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a well-known multi-criteria decision making (MCDM) method technique, to evaluate the relative merits of different forecasting models. Ultimately, this study aims to provide researchers and practitioners engaged in EV charging demand forecasting with clear analytical criteria and practical guidelines for model selection.