A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting

Shengxiang Lv, Lin Wang and Sirui Wang ()
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Shengxiang Lv: School of Business Administration, Guangdong University of Finance & Economics, Guangzhou 510320, China
Lin Wang: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
Sirui Wang: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2023, vol. 16, issue 4, 1-18

Abstract: This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition (VMD) is adopted to decompose the wind speed data into multiple subseries where each subseries contains unique local characteristics, and all the subseries are converted into two-dimensional samples. (b) A gated recurrent unit (GRU) is sequentially modeled based on the obtained samples and makes the predictions for future wind speed. (c) The grid search with rolling cross-validation (GSRCV) is designed to simultaneously optimize the key parameters of VMD and GRU. To evaluate the effectiveness of the proposed VMD-GRU-GSRCV model, comparative experiments based on hourly wind speed data collected from the National Renewable Energy Laboratory are implemented. Numerical results show that the root mean square error, mean absolute error, mean absolute percentage error, and symmetric mean absolute percentage error of this proposed model reach 0.2047, 0.1435, 3.77%, and 3.74%, respectively, which outperform the benchmark predictions using popular parameter optimization methods, data processing techniques, and hybrid neural network forecasting models.

Keywords: wind speed forecasting; variational mode decomposition; gated recurrent unit; grid search; rolling cross-validation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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