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Forecasting of renewable energy generation provides key insights which mayhelp with decision-making towards global decarbonisation. Renewable energygeneration can often be represented through cross-sectional hierarchies,whereby a single farm may have multiple individual generators. Hierarchicalforecasting through reconciliation has demonstrated a significant increase inthe quality of forecasts both theoretically and empirically. However, it is notevident whether forecasts generated by individual temporal and cross-sectionalaggregation can be superior to integrated cross-temporal forecasts and toindividual forecasts on more granular data. In this study, we investigate theaccuracies of different cross-sectional and cross-temporal reconciliationmethods using both linear regression and gradient boosting machine learning forforecasting wind farm power generation. We found that cross-temporalreconciliation is superior to individual cross-sectional reconciliation atmultiple temporal aggregations. Cross-temporally reconciled machine learningbase forecasts also demonstrated a high accuracy at coarser temporalgranularities, which may encourage adoption for short-term wind forecasts. Wealso show that linear regression can outperform machine learning models acrossmost levels in cross-sectional wind time series.

How to forecast power generation in wind farms? Insights from leveraging hierarchical structure