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Title: A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

Authors:
; ; ;
Publication Date:
Grant/Contract Number:
AC36-08GO28308
Type:
Publisher's Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 190; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-09-04 18:55:28; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Sponsoring Org:
USDOE
Country of Publication:
United Kingdom
Language:
English
OSTI Identifier:
1397067

Feng, Cong, Cui, Mingjian, Hodge, Bri-Mathias, and Zhang, Jie. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. United Kingdom: N. p., Web. doi:10.1016/j.apenergy.2017.01.043.
Feng, Cong, Cui, Mingjian, Hodge, Bri-Mathias, & Zhang, Jie. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. United Kingdom. doi:10.1016/j.apenergy.2017.01.043.
Feng, Cong, Cui, Mingjian, Hodge, Bri-Mathias, and Zhang, Jie. 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting". United Kingdom. doi:10.1016/j.apenergy.2017.01.043.
@article{osti_1397067,
title = {A data-driven multi-model methodology with deep feature selection for short-term wind forecasting},
author = {Feng, Cong and Cui, Mingjian and Hodge, Bri-Mathias and Zhang, Jie},
abstractNote = {},
doi = {10.1016/j.apenergy.2017.01.043},
journal = {Applied Energy},
number = C,
volume = 190,
place = {United Kingdom},
year = {2017},
month = {3}
}