A Transformer-Based Hybrid Neural Network Integrating Multiresolution Turbulence Intensity and Independent Modeling of Multiple Meteorological Features for Wind Speed Forecasting

Hongbin Liu, Ziyan Wang, Yizhuo Liu, Jie Zhou, Chen Chen, Haoyuan Ma, Xi Huang, Hongqing Wang () and Xiaodong Ji ()
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Hongbin Liu: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Ziyan Wang: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Yizhuo Liu: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Jie Zhou: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Chen Chen: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Haoyuan Ma: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Xi Huang: Department of Forestry (Urban Forestry), College of Forestry, Beijing Forestry University, Beijing 100083, China
Hongqing Wang: Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
Xiaodong Ji: Discipline of Civil Engineering, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China

Energies, 2025, vol. 18, issue 17, 1-38

Abstract: Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), the original wind speed series is decomposed into eight pairs of mean wind speeds and turbulence intensities at different time scales, which are then modeled and predicted in parallel using eight independent LSTM sub-models. Unlike traditional methods treating meteorological variables such as air pressure, temperature, and wind direction as static input features, WaveNet, LSTM, and TCN neural networks are innovatively adopted here to independently model and forecast these meteorological series, thoroughly capturing their dynamic influences on wind speed. Finally, a Transformer-based self-attention mechanism dynamically integrates multiple outputs from the four sub-models to generate final wind speed predictions. Experimental results averaged over three datasets demonstrate superior accuracy and robustness, with MAE, RMSE, MAPE, and R 2 values around 0.65, 0.87, 23.24%, and 0.92, respectively, for a 6 h forecast horizon. Moreover, the proposed framework consistently outperforms all baselines across four categories of comparative experiments, showing strong potential for practical applications in wind power dispatching.

Keywords: wind speed forecasting; turbulence intensity; neural networks; Transformer (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: 2025
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