Short-term wind power prediction with a new PCC-GWO-VMD and BiGRU hybrid model enhanced by attention mechanism DOI
Xiaoyu Zhang,

X. S. Qin,

Jiwei Qin

et al.

Journal of Renewable and Sustainable Energy, Journal Year: 2025, Volume and Issue: 17(3)

Published: May 1, 2025

The integration of wind power into the grid significantly depends on accuracy and reliability prediction. However, task prediction faces significant challenges due to stochastic nature speed. This study proposes a novel deep hybrid short-term model: PCC-GWO-VMD-BiGRU-Attention. model integrates Pearson correlation coefficient (PCC), variational mode decomposition (VMD), gray wolf optimization (GWO), attention mechanism optimized bidirectional gated recurrent unit (BiGRU-Attention) enhance robustness, while quantifying uncertainty through ensemble methods. First, PCC is used identify key factors affecting power, thereby improving model's computational performance. Second, GWO can dynamically adjust parameters [K, α] in VMD for optimal input time series, accuracy. Finally, BiGRU-Attention utilized extract global temporal features historical sequences, focuses information sequences further Experimental results show that, compared other learning models, this achieves highest with an root mean square error 0.2677 Megawatt (MW), absolute 0.1509 0.0717 determination (R2) 0.9605. method has practical value contributes ensuring safe operation farms.

Language: Английский

A physics-informed temporal convolutional network-temporal fusion transformer hybrid model for probabilistic wind speed predictions with quantile regression DOI

Lihua Mi,

Yan Han,

Lizhi Long

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136302 - 136302

Published: April 1, 2025

Language: Английский

Citations

0
Short-term wind power prediction with a new PCC-GWO-VMD and BiGRU hybrid model enhanced by attention mechanism DOI
Xiaoyu Zhang,

X. S. Qin,

Jiwei Qin

et al.

Journal of Renewable and Sustainable Energy, Journal Year: 2025, Volume and Issue: 17(3)

Published: May 1, 2025

The integration of wind power into the grid significantly depends on accuracy and reliability prediction. However, task prediction faces significant challenges due to stochastic nature speed. This study proposes a novel deep hybrid short-term model: PCC-GWO-VMD-BiGRU-Attention. model integrates Pearson correlation coefficient (PCC), variational mode decomposition (VMD), gray wolf optimization (GWO), attention mechanism optimized bidirectional gated recurrent unit (BiGRU-Attention) enhance robustness, while quantifying uncertainty through ensemble methods. First, PCC is used identify key factors affecting power, thereby improving model's computational performance. Second, GWO can dynamically adjust parameters [K, α] in VMD for optimal input time series, accuracy. Finally, BiGRU-Attention utilized extract global temporal features historical sequences, focuses information sequences further Experimental results show that, compared other learning models, this achieves highest with an root mean square error 0.2677 Megawatt (MW), absolute 0.1509 0.0717 determination (R2) 0.9605. method has practical value contributes ensuring safe operation farms.

Language: Английский

Citations

0