Quantile Forecast of Renewable Energy Generation Based on Indicator Gradient Descent and Deep Residual BiLSTM

Hu, T, Li, K orcid.org/0000-0001-6657-0522, Ma, H et al. (2 more authors) (Accepted: 2021) Quantile Forecast of Renewable Energy Generation Based on Indicator Gradient Descent and Deep Residual BiLSTM. Control Engineering Practice. ISSN 0967-0661 (In Press)

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Authors/Creators:
Dates:
  • Accepted: 4 June 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 10 Jun 2021 14:45
Last Modified: 10 Jun 2021 14:45
Status: In Press
Publisher: Elsevier

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