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

TL;DR

This paper enhances wind power forecast accuracy by integrating cross-sectional and temporal hierarchical structures, demonstrating that cross-temporal reconciliation improves predictions, especially when combined with machine learning at various temporal scales.

Contribution

It introduces a novel approach of cross-temporal hierarchical reconciliation for wind power forecasting, improving accuracy over existing methods.

Findings

Cross-temporal reconciliation outperforms individual cross-sectional methods.

Machine learning forecasts with cross-temporal reconciliation are highly accurate at coarser granularities.

Insights provided for decision-makers on optimal forecasting methods across different horizons.

Abstract

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.