Deep Transfer Learning for Detecting Electric Vehicles Highly Correlated Energy Consumption Parameters-Reference-Cited by-同舟云学术

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Deep Transfer Learning for Detecting Electric Vehicles Highly Correlated Energy Consumption Parameters

Published:2024-08 Issue:8 Volume:5 Page:4087-4100
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Teimoori Zeinab¹^ORCID,Yassine Abdulsalam²^ORCID,Lu Chaoru³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, ON, Canada

2. Department of Software Engineering, Lakehead University, Thunder Bay, ON, Canada

3. Department of Built Environment, Oslo Metropolitan University, Oslo, Norway

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/10635096/10414363.pdf?arnumber=10414363

Reference35 articles.

1. Blockchain-Based Intelligent Charging Station Management System Platform

2. Influence of Geodemographic Factors on Electricity Consumption and Forecasting Models

3. A Secure Cloudlet-Based Charging Station Recommendation for Electric Vehicles Empowered by Federated Learning

4. Electric car range and 5 reasons why your range anxiety is unwarranted;Barlingen,2022

5. Internet of Things based real-time electric vehicle load forecasting and charging station recommendation

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. Leveraging machine learning for efficient EV integration as mobile battery energy storage systems: Exploring strategic frameworks and incentives;Journal of Energy Storage;2024-07

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