WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network

dc.contributor.author	Acikgoz, Hakan
dc.contributor.author	Budak, Umit
dc.contributor.author	Korkmaz, Deniz
dc.contributor.author	Yildiz, Ceyhun
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2021
dc.identifier.issn	0360-5442
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9809
dc.identifier.uri	https://doi.org/10.1016/j.energy.2021.121121
dc.description.abstract	This paper introduces a novel deep neural network (WSFNet) to efficiently forecast multi-step ahead wind speed. WSFNet forms the basis of the stacked convolutional neural network (CNN) with dense connections of different blocks equipped with the channel a
dc.language.iso	English
dc.publisher	Pergamon-Elsevıer Scıence Ltd
dc.source	Energy
dc.title	WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network
dc.type	Article
dc.identifier.doi	10.1016/j.energy.2021.121121
dc.identifier.wos	WOS:000681276500004
dc.identifier.volume	233

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