Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

dc.contributor.author	Mehdizadeh, S.
dc.contributor.author	Sales, A.K.
dc.contributor.author	Safari, M.J.S.
dc.date.accessioned	2021-01-25T19:32:36Z
dc.date.available	2021-01-25T19:32:36Z
dc.date.issued	2020
dc.identifier	10.1007/s42452-020-2830-0
dc.identifier.issn	2523-3963
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/7276
dc.description.abstract	Wind speed data are of particular importance in the design and management of wind power projects. In the current study, three types of linear time series models including autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) w
dc.language.iso	English
dc.publisher	SPRINGER INTERNATIONAL PUBLISHING AG
dc.title	Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models
dc.type	Article
dc.relation.volume	2
dc.relation.issue	6
dc.description.woscategory	Multidisciplinary Sciences
dc.description.wosresearcharea	Science & Technology - Other Topics
dc.identifier.wosid	WOS:000538087000052

Files in this item

Files	Size	Format	View
There are no files associated with this item.