Based on the electric vehicle (EV) arrival times and the duration of EV connection to the charging station, we identify charging patterns and derive groups of charging stations with similar charging patterns applying two approaches. The ruled based approach derives the charging patterns by specifying a set of time intervals and a threshold value. In the second approach, we combine the modified l-p norm (as a matrix dissimilarity measure) with hierarchical clustering and apply them to automatically identify charging patterns and groups of charging stations associated with such patterns. A dataset collected in a large network of public charging stations is used to test both approaches. Using both methods, we derived charging patterns. The first, rule-based approach, performed well at deriving predefined patterns and the latter, hierarchical clustering, showed the capability of delivering unexpected charging patterns.
Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks
Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Data Science & Intelligent Systems (DSIS) Research Group, University of Bonn（波恩大学数据科学与智能系统（DSIS）研究组）
Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations
organization= Center for Environmental Intelligence, VinUniversity , city= Hanoi , country= Vietnam；organization= College of Engineering \& Computer Science, VinUniversity , city= Hanoi , country= Vietnam
Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations
Shenzhen Power Supply Bureau Co., Ltd.（深圳供电局有限公司）；Electric Power Research Institute, CSG（中国南方电网电力科学研究院）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Tsinghua University（清华大学）
AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR)（新加坡科技研究局资讯通信研究院（I2R））
