Hybrid model with temporal convolutional network and transformer encoder for privacy-preserving wind power forecasting

Advances in Manufacturing, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

Language: Английский

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Fault diagnosis of electric ship propulsion motor based on modified secondary decomposition and DE-BCECAN under strong noise background

Ocean Engineering, Journal Year: 2025, Volume and Issue: 331, P. 121264 - 121264

Published: April 23, 2025

Language: Английский

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Bioenergy Market Predictions using AI: Integrating Climate Change and Green Finance

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123328 - 123328

Published: April 1, 2025

Language: Английский

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

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

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A Novel Coupled Model for Monthly Rainfall Prediction Based on ESMD-EWT-SVD-LSTM

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(9), P. 3297 - 3312

Published: March 19, 2024

Language: Английский

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