This work investigates an attention-based approach for the combination of wind power forecasts, in which soft-weights are dynamically assigned to individual power forecasts through a learned attention mechanism. In contrast to traditional methods like linear regression models, an attention mechanism computes context-sensitive weights at inference time, allowing the combination to respond dynamically to the specific characteristics of each forecast situation. Additionally, the learned weighting structure captures non-linear dependencies between base forecasters, which static combination rules cannot express. Unlike conventional combination approaches that rely on repeated retraining to keep model weights aligned with recent observations, the proposed method follows a semi-adaptive design: once trained, the attention mechanism adapts to the forecast combination implicitly through its learned weighting structure, witho