The growing penetration of intermittent, renewable generation in US power grids results in increased operational uncertainty. In that context, accurate forecasts are critical, especially for wind generation, which exhibits large variability and is historically harder to predict. To overcome this challenge, this work proposes a novel Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques to accurately predict wind power at the asset, bundle, and fleet level. Notably, our approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks (fleet-level time series) and proposes new asset-bundling criteria to capture the spatio-temporal dynamics of wind power time series. Extensive numerical experiments are conducted on an industry-size dataset of wind farms, demonstrating the benefits of BPR, which consistently and significantly improves forecast accuracy over the baseline approach, especially at the fleet level.