SHORT-TERM PREDICTION OF WIND POWER BASED ON NRBO-LSTM-ATTENTION MODIFIED WIND SPEED UNDER OPTIMIZED VARIATIONAL MODE DECOMPOSITION

Yang Yuanwen; Huang Zhao; Wang Xin; Guo Zhiwei; Zhang Liu

doi:10.19912/j.0254-0096.tynxb.2024-1492

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 441-449. DOI: 10.19912/j.0254-0096.tynxb.2024-1492

SHORT-TERM PREDICTION OF WIND POWER BASED ON NRBO-LSTM-ATTENTION MODIFIED WIND SPEED UNDER OPTIMIZED VARIATIONAL MODE DECOMPOSITION

Yang Yuanwen¹, Huang Zhao^1,2, Wang Xin¹, Guo Zhiwei², Zhang Liu²

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Abstract

To enhance the accuracy of wind speed prediction in numerical weather prediction （NWP）, both NWP-derived wind speeds and actual wind farm wind speeds are input into a variational mode decomposition （VMD） optimized by the global search strategy whale optimization algorithm （GSWOA） for decomposition. The decomposed actual wind speed components serve as training targets, while the corresponding NWP wind speed components are fed into the Newton-Raphson-based optimizer-long short-term memory with attention mechanism （NRBO-LSTM-Attention） model. The output components are linearly superimposed to replace the original NWP wind speeds. Subsequently, the corrected NWP data and wind farm data undergo outlier cleaning using algorithms such as isolation forest and Ransac. The processed data is finally input into the NRBO-LSTM-Attention model to predict future power output. Simulation results demonstrate that the corrected NWP wind speeds are closer to actual wind speeds, with evaluation metrics MAE and RMSE decreasing by 11.45% and 19.82%, respectively, and R² increasing by 31.24%. Additionally, the power prediction model exhibits superior performance, with MAE and RMSE reduced by 11.36% and 10.43%, respectively, and R² improved by 3.42%.

Key words

wind farm / wind speed / variational mode decomposition / neural networks / Newton-Raphson-based optimizer / attention mechanism / power forecasting

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Yang Yuanwen, Huang Zhao, Wang Xin, Guo Zhiwei, Zhang Liu. SHORT-TERM PREDICTION OF WIND POWER BASED ON NRBO-LSTM-ATTENTION MODIFIED WIND SPEED UNDER OPTIMIZED VARIATIONAL MODE DECOMPOSITION[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 441-449 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1492

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