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.

Details

Title
Forecasting wind speed using a reinforcement learning hybrid ensemble model: a high-speed railways strong wind signal prediction study in Xinjiang, China
Author
Liu, Bin 1 ; Pan, Xinmin 2 ; Yang, Rui 3 ; Zhu, Duan 3 ; Li, Ye 3 ; Shi, Yin 3 ; Nikitas, Nikolaos 4 ; Liu, Hui 3 

 Research Institute of Science and Technology of China Railway Urumqi Group Co., Ltd. , Urumqi 830011, Xinjiang , China 
 Xinjiang Uygur Autonomous Region Meteorological Service , Urumqi 830002, Xinjiang , China 
 School of Traffic and Transportation Engineering, Central South University , Changsha 410075, Hunan , China 
 School of Civil Engineering, University of Leeds , LS2 9JT, Leeds , UK 
Publication year
2023
Publication date
Sep 2023
Publisher
Oxford University Press
e-ISSN
26314428
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3171759585
Copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of Central South University Press. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.