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.

翻译：可再生能源发电预测为全球脱碳决策提供了关键洞察。可再生能源发电量通常可通过截面层级结构表示，其中单个风电场可能包含多个独立发电机。理论上和实证研究均表明，通过协调法实现的层级预测能显著提高预测质量。然而，目前尚不明确：由独立时序与截面聚合生成的预测，能否优于跨时间-截面集成预测以及基于更细粒度数据的独立预测？本研究采用线性回归与梯度提升机器学习方法，探究不同截面及跨时间-截面协调法对风电场发电功率预测的准确性。研究发现：在多种时间聚合尺度下，跨时间-截面协调法优于独立截面协调法；经跨时间-截面协调的机器学习基础预测在较粗时间粒度下仍保持高精度，这或将促进其在短期风电预测中的实际应用。此外，研究还表明，在风电截面时间序列的大多数层级中，线性回归的表现可超越机器学习模型。