A Physics-Regularized Gated Model with Patch-Based Temporal Encoding for Short-Term Probabilistic Wind Farm Power Forecasting

Abstract

: Accurate probabilistic wind farm power forecasting is essential for reserve scheduling, dispatch decision-making, and risk-aware operation under high levels of wind power penetration. However, short-term wind power sequences exhibit strong nonstationarity, heterogeneous environmental variables exhibit time-varying predictive relevance under different meteorological regimes and operating states, and historical operating states and future numerical weather prediction (NWP) variables contribute differently over the forecasting horizon. In addition, direct quantile forecasts may suffer from quantile crossing or physically inconsistent wind-speed-power responses. To address these issues, this paper proposes a physics-regularized gated model for short-term probabilistic wind farm power forecasting, using hourly Local Peak Power (LPP) as the operational evaluation target. The temporal encoder follows the PatchTST patching strategy to capture both short-term ramping behavior and longer-range temporal dependence. A group-gated environmental variable selection and fusion module is designed to adaptively emphasize physically relevant meteorological variable groups and conditionally integrate historical representations with future NWP information. Moreover, a monotone multi-quantile prediction structure with a physics-based regularization term is introduced to improve probabilistic coherence and wind-speed-power consistency. Experiments are conducted against ten representative baselines covering empirical, tree-based ensemble, recurrent, convolutional, and Transformer-based probabilistic forecasting methods. The proposed model achieves the lowest mean absolute error (MAE), root mean squared error (RMSE), and continuous ranked probability score (CRPS) and obtains a 90% prediction interval coverage probability (PICP90) of 0.9001 and a 90% prediction interval normalized average width (PINAW90) of 0.3123, indicating near-nominal interval coverage with a moderate interval width. Relative to the strongest baseline for each metric, the proposed model reduces MAE by 4.92% compared with LightGBM-QR and reduces RMSE and CRPS by 2.78% and 1.94% compared with XGBoost-QR, respectively. Statistical significance analysis then confirms the overall performance gains, while ablation, interpretability, and sensitivity analyses verify the effectiveness of group-gated fusion, the future NWP branch, and physics-based regularization. These results demonstrate that the proposed model provides accurate, reliable, and physically consistent probabilistic forecasts for short-term wind farm operation.