A Dual-Track Feature-Enhanced Physics-Informed Model for Accurate Wind Power Forecasting with Physical Consistency

In response to stochastic fluctuations in large-scale wind power integration and the resulting peak-shaving challenges, high-precision forecasting with physical consistency is essential for grid safety. To address the inefficiency of physical models and poor interpretability of data-driven methods, this paper proposes a hybrid FCM-WGM-BiLSTM-Transformer (FW-BTP) framework integrating Fuzzy C-Means (FCM) clustering, Weighted Grey Model (WGM) trend extraction, and a coupled BiLSTM-Transformer module. Physical prior knowledge—wind turbine power curves—is embedded into the loss function via a Physics-Guided Loss Regularization (PGL) mechanism. Validated on measured data from a Xinjiang wind farm, the model achieves an R2 of 0.9967, MAE of 6.11, and RMSE of 11.19. The proposed model reduces R2 by 37% compared to the newer model KAN, and compared to the better-performing recurrent baseline model (BiLSTM, MAE = 8.75 MW), the proposed FW-BTP model reduces the MAE by 30% (to 6.12 MW). Ablation studies confirm the WGM reduces LogCosh loss from 9.57 to 5.12, and SHAP analysis verifies sensitivity to trend and physical wind speed features. The framework balances accuracy, robustness, and interpretability, supporting refined scheduling in modern power systems.