Periodic-enhanced informer model for short-term wind power forecasting using SCADA data

Liu, Z.-H., Li, L.-W., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (3 more authors) (2025) Periodic-enhanced informer model for short-term wind power forecasting using SCADA data. IEEE Transactions on Sustainable Energy. ISSN 1949-3029

Abstract

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Item Type: Article
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Copyright, Publisher and Additional Information:

© 2025 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Sustainable Energy is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: SCADA; wind power forecasting; TCN; informer; maximum information coefficient
Dates:
  • Submitted: 13 July 2024
  • Accepted: 31 March 2025
  • Published (online): 8 April 2025
  • Published: 8 April 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering
Funding Information:
Funder
Grant number
ROYAL SOCIETY
IEC\NSFC\223266
Depositing User: Symplectic Sheffield
Date Deposited: 08 Apr 2025 12:38
Last Modified: 10 Apr 2025 08:41
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TSTE.2025.3558726
Open Archives Initiative ID (OAI ID):

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