A Physics-Constrained Dual-Stream Dynamic Framework for Wind Power Forecasting Under Extreme Weather

Accurate wind power forecasting is essential for ensuring power grid stability and facilitating the large-scale integration of renewable energy, yet it faces significant challenges due to the randomness, variability, and intermittency of wind resources and the increasing frequency of extreme weather events. Existing data-driven approaches often struggle to balance temporal continuity with meteorological sensitivity, leading to lag effects during rapid fluctuations, and frequently generate predictions that violate physical domain knowledge. To address these limitations, this paper proposes a dual-stream architecture to decouple temporal dependencies and spatial–meteorological mappings, utilizing a Physics-Informed GRU (PI-GRU) and an Enhanced Random Forest (ERF). Both streams are strictly bounded by physical constraints. Furthermore, a scenario-aware adaptive fusion mechanism is introduced to dynamically adjust the model’s reliance on each stream based on real-time wind speed gradients and volatility indices. Extensive experiments were conducted using a comprehensive dataset from three coastal wind farms over 8 months, encompassing stable regimes and extreme weather events. Evaluating across both 1-day and 4-day forecast horizons, the results demonstrate that our method significantly outperforms state-of-the-art baselines, proving its robustness and practical value for grid security and dispatch optimization.