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）
Ashutosh Sao, Nicolas Tempelmeier, Elena Demidova
发表信息：
24th IEEE International Conference on Intelligent Transportation Systems - ITSC2021
With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effectively forecast the charging station occupation. We exploit static information, such as the mean occupation concerning the time of day, to learn the specific charging station patterns. We supplement such static data with dynamic information reflecting the preceding charging station occupation and temporal information such as daytime and weekday. Our model efficiently fuses dynamic and static information to facilitate accurate forecasting. We evaluate the proposed model on a real-world dataset containing 593 charging stations in Germany, covering August 2020 to December 2020. Our experiments demonstrate that DFDS outperforms the baselines by 3.45 percent points in F1-score on average.
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））
Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks
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）
