Yang, R, Liu, H, Nikitas, N orcid.org/0000-0002-6243-052X et al. (3 more authors) (2022) Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach. Energy, 239 (Part B). 122128. ISSN 0360-5442
Abstract
The safe and stable operation of wind power systems requires the support of wind speed prediction. To ensure the controllability and stability of smart grid dispatching, a novel hybrid model consisting of data-adaptive decomposition, reinforcement learning ensemble, and improved error correction is established for short-term wind speed forecasting. In decomposition module, empirical wavelet transform algorithm is used to adaptively disassemble and reconstruct the wind speed series. In ensemble module, Q-learning is utilized to integrate gated recurrent unit, bidirectional long short-term memory, and deep belief network. In error correction module, wavelet packet decomposition and outlier-robust extreme learning machine are combined to developing predictable components. An appropriate correction shrinkage rate is used to obtain the best correction effect. Ljung-Box Q-Test is utilized to judge the termination of the error correction iteration. Four real data are utilized to validate model performance in the case study. Experimental results show that: (a) The proposed hybrid model can accurately capture the changes of wind data. Taking 1-step prediction results as an example, the mean absolute errors for site #1, #2, #3, and #4 are 0.0829 m/s, 0.0661 m/s, 0.0906 m/s, and 0.0803 m/s, respectively; (b) Compared with several state-of-the-art models, the proposed model has the best prediction performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. All rights reserved. This is an author produced version of an article, published in Energy. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Short-Term Wind Speed Prediction; Adaptive Data Decomposition; Q-Learning Ensemble Strategy; Improved Multiple Error Correction Technique |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Funding Information: | Funder Grant number National Highways Limited fka Highways England Co Ltd Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Sep 2021 14:05 |
Last Modified: | 20 Jan 2023 14:33 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.energy.2021.122128 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178507 |