WindPower-SAFusion: A Sparse-Attention and Multi-Scale Fusion Model for Wind-Power Forecasting

Accurate wind-power forecasting is essential for grid scheduling when renewable generation becomes highly variable. This study developed WindPower-SAFusion, an Informer-inspired forecasting model designed for long wind-power sequences. The framework is built around three complementary designs. First, ProbSparse self-attention is used to lower the attention cost from O(L2) to O(LlogL) while retaining informative temporal dependencies. Second, convolutional distillation is embedded in the encoder to summarize local fluctuations and form multi-scale representations. Third, historical theoretical power and wind speed are fused in a recursive forecasting scheme for multi-step prediction. The model is evaluated using measured data from the Daliang Wind Farm in Guazhou, Gansu Province, China. Experiments conducted using 1-day, 3-day, and 7-day horizons show that WindPower-SAFusion obtained lower errors and higher explanatory ability than the selected statistical and deep learning baselines. The ablation results further confirm the contributions of sparse attention, convolutional feed-forward extraction, and sequence distillation. These findings indicate that the proposed framework can provide an effective data-driven tool for wind-farm dispatching and power-management applications.