How to forecast power generation in wind farms? Insights from leveraging hierarchical structure
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Forecasting of renewable energy generation provides key insights which may help with decision-making towards global decarbonisation. Renewable energy generation can often be represented through cross-sectional hierarchies, whereby a single farm may have multiple individual generators. Hierarchical forecasting through reconciliation has demonstrated a significant increase in the quality of forecasts both theoretically and empirically. However, it is not evident whether forecasts generated by individual temporal and cross-sectional aggregation can be superior to integrated cross-temporal forecasts and to individual forecasts on more granular data. In this study, we investigate the accuracies of different cross-sectional and cross-temporal reconciliation methods using both linear regression and gradient boosting machine learning for forecasting wind farm power generation. We found that cross-temporal reconciliation is superior to individual cross-sectional reconciliation at multiple temporal aggregations. Cross-temporally reconciled machine learning base forecasts also demonstrated a high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. We also show that linear regression can outperform machine learning models across most levels in cross-sectional wind time series.
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