NWP-Conditioned Dynamic Simplicial Neural Networks for Joint Day-Ahead Forecasting of Regional Wind Farm Clusters

Regional day-ahead wind power forecasting (WPF) requires spatial dependence to adapt to forecast weather. Many existing graph models mainly capture pairwise relations derived from static geography or historical correlations, limiting their representation of multi-farm responses under changing wind fields. This paper proposes AeroSimp-Lite, an NWP-conditioned dynamic simplicial neural network that represents farms, pairwise relations, and three-farm interactions as nodes, edges, and triangles. A fixed sparse complex preserves simplicial closure, while forecast wind vectors dynamically condition interactions at each lead time. Hodge-inspired filtering integrates multi-order information, and a temporal residual decoder corrects a local NWP-based power prior. Regional-consistency and ramp losses constrain aggregate production and rapid changes. Experiments on onshore and offshore clusters demonstrate improved site-level and regional accuracy with moderate computational cost. In the model-order comparison, dynamic edges reduce regional nMAE by 7.1% onshore and 12.0% offshore relative to the node-only model, while adding triangle interactions yields further reductions of 3.8% and 4.5%, respectively, relative to Dynamic-GNN. Ablation results show that dynamic edges provide the main gain, while triangles offer smaller, metric-dependent improvements. However, the lower offshore ramp F1 shows that average accuracy gains do not extend to every objective. AeroSimp-Lite provides a practical approach to modeling weather-dependent higher-order spatial interactions without claiming atmospheric causality.