  Renewable energy generation is of utmost importance for global
decarbonization. Forecasting renewable energies, particularly wind energy, is
challenging due to the inherent uncertainty in wind energy generation, which
depends on weather conditions. Recent advances in hierarchical forecasting
through reconciliation have demonstrated a significant increase in the quality
of wind energy forecasts for short-term periods. We leverage the
cross-sectional and temporal hierarchical structure of turbines in wind farms
and build cross-temporal hierarchies to further investigate how integrated
cross-sectional and temporal dimensions can add value to forecast accuracy in
wind farms. We found that cross-temporal reconciliation was superior to
individual cross-sectional reconciliation at multiple temporal aggregations.
Additionally, machine learning based forecasts that were cross-temporally
reconciled demonstrated high accuracy at coarser temporal granularities, which
may encourage adoption for short-term wind forecasts. Empirically, we provide
insights for decision-makers on the best methods for forecasting high-frequency
wind data across different forecasting horizons and levels.
