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Renewable energy generation is of utmost importance for globaldecarbonization. Forecasting renewable energies, particularly wind energy, ischallenging due to the inherent uncertainty in wind energy generation, whichdepends on weather conditions. Recent advances in hierarchical forecastingthrough reconciliation have demonstrated a significant increase in the qualityof wind energy forecasts for short-term periods. We leverage thecross-sectional and temporal hierarchical structure of turbines in wind farmsand build cross-temporal hierarchies to further investigate how integratedcross-sectional and temporal dimensions can add value to forecast accuracy inwind farms. We found that cross-temporal reconciliation was superior toindividual cross-sectional reconciliation at multiple temporal aggregations.Additionally, machine learning based forecasts that were cross-temporallyreconciled demonstrated high accuracy at coarser temporal granularities, whichmay encourage adoption for short-term wind forecasts. Empirically, we provideinsights for decision-makers on the best methods for forecasting high-frequencywind data across different forecasting horizons and levels.

Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure