Wind farms need fast ways to predict power and structural fatigue without costly repeated simulations. We compared different representations of wind across a turbine rotor. Spatial averages were sufficient for power, but detailed wind-field information improved predictions for certain fatigue loads. A compact PCA representation achieved the lowest power-prediction error, while a spatial CNN model performed best for fatigue. We show that the most effective approach depends on the prediction task.