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Short-term Wind Power Prediction Based on CEEMDAN De-composition and Spatiotemporal Feature Fusion

Author(s): (School of Electrical Engineering, Xi'an University of Technology, Xi'an, China)
(School of Electrical Engineering, Xi'an University of Technology, Xi'an, China)
(School of Electrical Engineering, Xi'an University of Technology, Xi'an, China)
(School of Electrical Engineering, Xi'an University of Technology, Xi'an, China)
(School of Electrical Engineering, Xi'an University of Technology, Xi'an, China)
Medium: journal article
Language(s): English
Published in: Proceedings of the Institution of Civil Engineers - Energy, , n. 4, v. 175
Page(s): 1-27
DOI: 10.1680/jener.21.00104
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1680/jener.21.00104.
  • About this
    data sheet
  • Reference-ID
    10665203
  • Published on:
    09/05/2022
  • Last updated on:
    10/12/2022
 
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