Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Laib, O., Khadir, M. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2019) Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks. Energy, 177. pp. 530-542. ISSN 0360-5442

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 Elsevier. This is an author produced version of a paper subsequently published in Energy. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Hourly natural gas consumption; Clustering; Time series; Artificial neural network; Long short term memory; Day-ahead forecast
Dates:
  • Accepted: 13 April 2019
  • Published (online): 17 April 2019
  • Published: 15 June 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 29 Apr 2019 09:11
Last Modified: 17 Apr 2020 00:38
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.energy.2019.04.075

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