EVisionary: A Prediction Platform for Electric Vehicle Charging Capacity based on the Impact Analysis of Climate Factors-Reference-Cited by-同舟云学术

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EVisionary: A Prediction Platform for Electric Vehicle Charging Capacity based on the Impact Analysis of Climate Factors

Published:2024-03-15 Issue: Volume:8 Page:1-7
ISSN:
Container-title:2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
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Author:

Li David Chunhu¹,Ku Pei-Chun¹,Tai Wei¹,Lan Yu-Ching¹,Kingsang Joener Lusechemad¹

Affiliation:

1. Ming Chuan University,Information Technology and Management Program,Taoyuan City,Taiwan, ROC

Funder

National Science and Technology Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10502391/10503583/10503691.pdf?arnumber=10503691

Reference16 articles.

1. Self-Attention-Based Machine Theory of Mind for Electric Vehicle Charging Demand Forecast

2. Hierarchical High-Resolution Load Forecasting for Electric Vehicle Charging: A Deep Learning Approach

3. Macroscopic Big Data Analysis and Prediction of Driving Behavior With an Adaptive Fuzzy Recurrent Neural Network on the Internet of Vehicles

4. EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs

5. Resilience-oriented operation of smart grids by rescheduling of energy resources and electric vehicles management during extreme weather condition

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Integrated Extended Kalman Filter and Deep Learning Platform for Electric Vehicle Battery Health Prediction;Applied Sciences;2024-05-21

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