Day-Ahead Spatiotemporal Wind Speed Forecasting Based on a Hybrid Model of Quantum and Residual Long Short-Term Memory Optimized by Particle Swarm Algorithm.
Y. Hong, und J. Santos. IEEE Syst. J., 17 (4):
6081-6092(Dezember 2023)
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%0 Journal Article
%1 journals/sj/HongS23
%A Hong, Ying-Yi
%A Santos, Jay Bhie D.
%D 2023
%J IEEE Syst. J.
%K dblp
%N 4
%P 6081-6092
%T Day-Ahead Spatiotemporal Wind Speed Forecasting Based on a Hybrid Model of Quantum and Residual Long Short-Term Memory Optimized by Particle Swarm Algorithm.
%U http://dblp.uni-trier.de/db/journals/sj/sj17.html#HongS23
%V 17
@article{journals/sj/HongS23,
added-at = {2024-01-13T00:00:00.000+0100},
author = {Hong, Ying-Yi and Santos, Jay Bhie D.},
biburl = {https://www.bibsonomy.org/bibtex/2b740244adbf13869f9954701f0c2ef99/dblp},
ee = {https://doi.org/10.1109/JSYST.2023.3265982},
interhash = {7fece58a5335ca96c40e3374542ddaa3},
intrahash = {b740244adbf13869f9954701f0c2ef99},
journal = {IEEE Syst. J.},
keywords = {dblp},
month = {December},
number = 4,
pages = {6081-6092},
timestamp = {2024-04-08T19:32:36.000+0200},
title = {Day-Ahead Spatiotemporal Wind Speed Forecasting Based on a Hybrid Model of Quantum and Residual Long Short-Term Memory Optimized by Particle Swarm Algorithm.},
url = {http://dblp.uni-trier.de/db/journals/sj/sj17.html#HongS23},
volume = 17,
year = 2023
}
