Short-Term Wind Power Forecasting Based on ICEEMDAN-EWT Decomposition, Complexity-Adaptive Networks, and Error Correction

Short-term wind power forecasting is challenging because power output exhibits strong non-stationarity, stochastic fluctuations, and multi-scale temporal characteristics. This study proposes an ICEEMDAN-EWT forecasting framework whose principal methodological contribution is a complexity-based network assignment strategy. ICEEMDAN decomposes the original power series, and EWT further refines the highest-frequency intrinsic mode function. Zero-crossing rate, spectral entropy, and volatility are integrated into a complexity score, according to which TCN-BiLSTM-Attention, BiLSTM-BiGRU-Attention, and GRU are assigned to high-, medium-, and low-complexity components, respectively. The component forecasts are reconstructed, and a BiLSTM-Attention model is further employed for residual error correction. Experiments are conducted separately on wind power datasets from Turkey and Inner Mongolia. Relative to TCN, the proposed model reduces MAE, RMSE, and NMAPE by 87.67%, 89.51%, and 87.67% on Dataset A, and by 79.92%, 81.11%, and 79.92% on Dataset B, respectively. The results demonstrate the effectiveness of combining complexity-adaptive network assignment with multi-scale decomposition and residual correction for short-term wind power forecasting.