A Variational Autoencoder-Based Dimensionality Reduction Technique for Generation Forecasting in Cyber-Physical Smart Grids

Kaur, Devinder, Islam, Shama and Mahmud, Md Apel 2021, A Variational Autoencoder-Based Dimensionality Reduction Technique for Generation Forecasting in Cyber-Physical Smart Grids, in ICC 2021 : Proceedings of the IEEE International Conference on Communications Workshops, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/ICCWorkshops50388.2021.9473748.

Attached Files
Name Description MIMEType Size Downloads

Title A Variational Autoencoder-Based Dimensionality Reduction Technique for Generation Forecasting in Cyber-Physical Smart Grids
Author(s) Kaur, DevinderORCID iD for Kaur, Devinder orcid.org/0000-0002-2354-7960
Islam, ShamaORCID iD for Islam, Shama orcid.org/0000-0002-5302-5338
Mahmud, Md Apel
Conference name Communications Workshops. Conference (2021 : Montreal, Quebec)
Conference location Montreal, Quebec
Conference dates 2021/06/14 - 2021/06/23
Title of proceedings ICC 2021 : Proceedings of the IEEE International Conference on Communications Workshops
Publication date 2021
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Dimensionality reduction
energy forecasting
deep learning
renewable energy generation
posterior approximation
VAE-BiLSTM
ISBN 9781728194417
Language eng
DOI 10.1109/ICCWorkshops50388.2021.9473748
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30154966

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 4 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Tue, 31 Aug 2021, 11:49:27 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.