Liu, Z.H., Wang, C.-T., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (3 more authors) (2023) An adaptive interval construction based GRU model for short-term wind speed interval prediction using two phase search strategy. IEEE Open Journal of Signal Processing, 4. pp. 375-389. ISSN 2644-1322
Abstract
The application of wind power is greatly restricted due to the volatility and intermittency of wind. It is a challenging task to quantify the uncertainty of wind speed prediction. To tackle such a challenge, an adaptive interval construction-based gated recurrent unit (GRU) model is proposed for directly generating short-term wind speed prediction intervals in this paper, using the two phase search strategy to search the model parameters. Different from the traditional interval prediction techniques, in the proposed model an adaptive interval construction method is designed, where the target values of wind speed are characterized by two interval width adjustment variables which are used to determine the lower and upper bounds of the interval of wind speed. A two phase search strategy is designed to optimize the parameters. In Phase I, the dynamic inertia weight particle swarm optimization algorithm is used to search the two interval width adjustment variables. In Phase II, the GRU networks are trained using the root mean square prop (RMSProp) algorithm (an effective gradient-based optimizer) to fit the upper and lower bounds of the constructed intervals, respectively. The two phases are executed alternately, so as to obtain optimal prediction intervals. The performance of the proposed method is compared with eight other machine learning and deep learning methods, and the experimental results show that the proposed method outperforms the compared methods. It indicates that the proposed method can generate satisfactory and better prediction intervals compared with other methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Deep learning; time series; interval prediction; gated recurrent unit; dynamic inertia weight particle swarm optimization; short-term wind speed |
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) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/I011056/1 ROYAL SOCIETY IEC\NSFC\223266 NATURAL ENVIRONMENT RESEARCH COUNCIL NE/V002511/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Jul 2023 11:54 |
Last Modified: | 04 Oct 2024 13:30 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/OJSP.2023.3298251 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201601 |