A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting

Wind speed interval prediction is one of the most long-standing challenges because of the high uncertainty and the complex spatial–temporal correlation between wind turbines. In this paper, based on variational Bayesian inference, we propose a novel spatial–temporal generative autoencoder (STGAE) mo...

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Bibliographic Details

Published in:	Energy (Oxford) Vol. 282; p. 128946
Main Authors:	Ma, Long, Huang, Ling, Shi, Huifeng
Format:	Journal Article
Language:	English
Published:	01.11.2023
Subjects:	Bayesian theory energy prediction probability distribution spatial data uncertainty wind speed wind turbines
ISSN:	0360-5442
Online Access:	Get full text
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