Abstract
Ultra-short-term wind power forecasting is essential for secure grid operation and efficient energy management. However, accurate forecasting remains challenging because wind power series usually exhibit strong nonlinearity, stochasticity, and rapid local variations. To address this issue, this study proposes an integrated forecasting framework that combines intensity-directed causal preprocessing with prior-guided temporal modeling. Specifically, an Intensity-Directed Multi-Scale Causal Moving Average (ID-CMA) method is developed for strictly causal online preprocessing. By adaptively fusing causal moving averages at different temporal scales according to local variation intensity, the proposed method constructs a trend-residual representation that helps suppress noise while preserving rapidly changing characteristics. On this basis, a change-intensity-aware prior derived from the preprocessing stage is incorporated into the self-attention mechanism as an additive bias, allowing the model to place more emphasis on highly varying intervals during temporal dependency modeling. Experimental results on real wind power datasets confirm the effectiveness of the proposed framework for ultra-short-term wind power forecasting, while also indicating that the proposed preprocessing method remains effective across different forecasting models.