Laib, O., Khadir, M.T. and Mihaylova, L.S. orcid.org/0000-0001-5856-2223 (2018) A Gaussian process regression for natural gas consumption prediction based on time series data. In: 2018 21st International Conference on Information Fusion (FUSION). 2018 21st International Conference on Information Fusion (FUSION) , 10-13 Jul 2018, Cambridge, UK. IEEE , pp. 55-61. ISBN 978-0-9964527-6-2
Abstract
For several economical, financial and operational reasons, forecasting energy demand becomes a key instrument in energy system management. This paper develops a natural gas forecasting approach, which consists of two major phases: 1) it classifies the natural gas consumption daily pattern sequences into different groups with similar attributes. 2) the design and training of multiple autoregressive Gaussian Process models phase is carried out using the Algerian natural gas market data together with exogenous inputs consisting in weather (temperature) and calendar (day of the week, hour indicator) factors. The main novelty in this work consists of the investigation of multiple different clustering techniques for better analysis and clustering of natural gas consumption data. The impact of the obtained clusters, by each technique, is then summarized and evaluated with respect to the prediction accuracy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 ISIF. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Time series classification; gaussian process; load forecasting; natural gas consumption |
Dates: |
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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: | 24 Sep 2018 14:46 |
Last Modified: | 24 Sep 2018 20:29 |
Published Version: | https://doi.org/10.23919/ICIF.2018.8455447 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2018.8455447 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136105 |