Graph Neural Network Based Prediction of Data Traffic in Cyber-Physical Smart Power Grids-Reference-Cited by-同舟云学术

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Graph Neural Network Based Prediction of Data Traffic in Cyber-Physical Smart Power Grids

Published:2022-10-25 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
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Author:

Islam Md Aminul¹,Ismail Muhammad¹,Boyaci Osman²,Atat Rachad³,Shannigrahi Susmit¹

Affiliation:

1. Tennessee Technological University,Department of Computer Science,Cookeville,TN,USA

2. Texas A&M University,Department of Electrical and Computer Engineering,TX,USA

3. Texas A&M University at Qatar,Department of Electrical and Computer Engineering,Doha,Qatar

Funder

Qatar National Research Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9960863/9960962/09960963.pdf?arnumber=9960963

Reference21 articles.

1. City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

2. Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting

3. Is Classical LSTM more Efficient than Modern GCN Approaches in the Context of Traffic Forecasting?

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. Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks;Sustainable Energy, Grids and Networks;2024-06

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