Liu, B., Pan, X., Yang, R. et al. (5 more authors) (2023) Forecasting wind speed using a reinforcement learning hybrid ensemble model: a high-speed railways strong wind signal prediction study in Xinjiang, China. Transportation Safety and Environment, 5 (4). ARTN tdac064. ISSN 2631-6765
Abstract
Considering the application of wind-forecasting technology along the railway, it becomes an effective means to reduce the risk of train derailment and overturning. Accurate prediction of crosswinds can provide scientific guidance for safe train operation. To obtain more reliable wind-speed prediction results, this study proposes an intelligent ensemble forecasting method for strong winds along the high-speed railway. The method consists of three parts: the data preprocessing module, the hybrid prediction module and the reinforcement learning ensemble module. First, fast ensemble empirical model decomposition (FEEMD) is used to process the original wind speed data. Then, Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, non-linear autoregressive network with exogenous inputs (NARX) and deep belief network (DBN), three benchmark predictors with different characteristics are employed to build prediction models for all the sublayers of decomposition. Finally, Q-learning is utilized to iteratively calculate the combined weights of the three models, and the prediction results of each sublayer are superimposed to obtain the model output. The real wind speed data of two railway stations in Xinjiang are used for experimental comparison. Experiments show that compared with the single benchmark model, the hybrid ensemble model has better accuracy and robustness for wind speed prediction along the railway. The 1-step forecasting results mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of Q-learning-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s, 0.6509%, 0.1146 m/s, and 0.0458 m/s, 0.2709%, 0.0616 m/s, respectively. The proposed ensemble model is a promising method for railway wind speed prediction.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. Published by Oxford University Press on behalf of Central South University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
Keywords: | wind speed forecasting; high-speed railways; signal decomposition; reinforcement learning; ensemble model |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jan 2024 11:23 |
Last Modified: | 05 Jan 2024 11:23 |
Published Version: | http://dx.doi.org/10.1093/tse/tdac064 |
Status: | Published |
Publisher: | Oxford University Press (OUP) |
Identification Number: | 10.1093/tse/tdac064 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206598 |