A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing-Reference-Cited by-同舟云学术

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A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing

Published:2024-06 Issue: Volume:363 Page:123059
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Kuang Haoxuan^ORCID,Qu Haohao^ORCID,Deng Kunxiang^ORCID,Li Jun^ORCID

Funder

Department of Science and Technology of Guangdong Province

Science and Technology Planning Project of Guangdong Province

Publisher

Elsevier BV

Reference52 articles.

1. Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption;Powell;Nat Energy,2022

2. Global EV outlook 2022;IEA,2022

3. Dynamic pricing for electric vehicle extreme fast charging;Fang;IEEE Trans Intell Transp Syst,2021

4. Regression shrinkage and selection via the lasso;Tibshirani;J R Stat Soc Ser B Stat Methodol,1996

5. Spatial and temporal model of electric vehicle charging demand;Bae;IEEE Trans Smart Grid,2011

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. Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands;Applied Energy;2025-01

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