Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach-Reference-Cited by-同舟云学术

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Forecasting EV Charging Demand: A Graph Convolutional Neural Network-Based Approach

Published:2024-01-08 Issue: Volume:29 Page:1-6
ISSN:
Container-title:2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)
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Author:

Fahim Shahriar Rahman¹,Atat Rachad²,Kececi Cihat¹,Takiddin Abdulrahman³,Ismail Muhammad⁴,Davis Katherine R.¹,Serpedin Erchin¹

Affiliation:

1. Texas A&M University,Electrical & Computer Engineering Department,College Station,TX,USA,77843

2. Texas A&M University at Qatar,Electrical & Computer Engineering Department,Doha,Qatar,23874

3. Florida State University,Electrical & Computer Engineering Department,Tallahassee,FL,USA,32310

4. Tennessee Tech University,Department of Computer Science,Cookeville,TN,USA,38505

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10428118/10428123/10428726.pdf?arnumber=10428726

Reference27 articles.

1. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

2. Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior

3. A Spatial–Temporal model for grid impact analysis of plug-in electric vehicles

4. Time-Series Modeling of Aggregated Electric Vehicle Charging Station Load

5. Prediction of electric vehicle charging-power demand in realistic urban traffic networks

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. Charging change: Analysing the UK's electric vehicle infrastructure policies and market dynamics;Energy Policy;2024-08

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