An improved residual-based convolutional neural network for very short-term wind power forecasting

dc.contributor.author	Yildiz, Ceyhun
dc.contributor.author	Acikgoz, Hakan
dc.contributor.author	Korkmaz, Deniz
dc.contributor.author	Budak, Umit
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2021
dc.identifier.issn	0196-8904
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9874
dc.identifier.uri	https://doi.org/10.1016/j.enconman.2020.113731
dc.description.abstract	An accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy pro
dc.language.iso	English
dc.publisher	Pergamon-Elsevıer Scıence Ltd
dc.source	Energy Conversıon And Management
dc.title	An improved residual-based convolutional neural network for very short-term wind power forecasting
dc.type	Article
dc.identifier.doi	10.1016/j.enconman.2020.113731
dc.identifier.wos	WOS:000607501700001
dc.identifier.volume	228

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