@Article{ee.2026.076521,
AUTHOR = {Xiaolan Li, Jinyu Shen, Jinhuang Liang, Yanting Wang},
TITLE = {Ultra-Short-Term Wind Power Forecasting Based on Hierarchical Signal Refinement and Intelligently Optimized Deep Learning},
JOURNAL = {Energy Engineering},
VOLUME = {123},
YEAR = {2026},
NUMBER = {7},
PAGES = {--},
URL = {http://www.techscience.com/energy/v123n7/67746},
ISSN = {1546-0118},
ABSTRACT = {The intrinsic volatility and stochasticity of large-scale wind power generation pose significant challenges to grid stability. To address the limitations of conventional models in capturing strong non-stationarity, this study proposes a novel Multi-Stage Adaptive Forecasting Network (MSAF-Net). The framework features a hierarchical signal refinement strategy coupled with an intelligently optimized hybrid predictor. Initially, input redundancy is minimized via Pearson Correlation Coefficient (PCC) analysis to isolate significant meteorological variables. A two-phase decomposition-reconstruction mechanism is then implemented: the wind power series is first decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). To optimize the trade-off between signal complexity and computational cost, the resulting components are reconstructed based on Sample Entropy (SE), with the highest-complexity component specifically targeted for secondary denoising via Empirical Wavelet Transform (EWT). For the prediction stage, a hybrid architecture integrates Bidirectional Temporal Convolutional Networks (BiTCN) to extract multi-scale local features and Bidirectional Long Short-Term Memory (BiLSTM) networks to model long-term temporal dependencies. Crucially, an Attention Mechanism is embedded to weigh critical time steps, while the Sparrow Search Algorithm (SSA) automatically optimizes the network hyperparameters. Experimental results demonstrate that MSAF-Net achieves an RMSE of 41.59, MAE of 26.67, and MAPE of 1.36%. Notably, the proposed model achieves a 23.16% reduction in MAPE compared to the competitive CEEMDAN-EWT-LSTM benchmark, verifying its superior predictive accuracy and generalization capability.},
DOI = {10.32604/ee.2026.076521}
}