Wind power forecasting is essential for managing daily operations at wind farms and enabling market operators to manage power uncertainty effectively in demand planning. This paper explores advanced cross-temporal forecasting models and their potential to enhance forecasting accuracy. First, we propose a novel approach that leverages validation errors, rather than traditional in-sample errors, for covariance matrix estimation and forecast reconciliation. Second, we introduce decision-based aggregation levels for forecasting and reconciliation where certain horizons are based on the required decisions in practice. Third, we evaluate the forecasting performance of the models not only on their ability to minimize errors but also on their effectiveness in reducing decision costs, such as penalties in ancillary services. Our results show that statistical-based hierarchies tend to adopt less conservative forecasts and reduce revenue losses. On the other hand, decision-based reconciliation offers a more balanced compromise between accuracy and decision cost, making them attractive for practical use.

翻译：风电功率预测对于管理风电场的日常运营以及使市场运营商能够在需求规划中有效管理电力不确定性至关重要。本文探讨了先进的跨时间预测模型及其在提升预测精度方面的潜力。首先，我们提出了一种新颖的方法，该方法利用验证误差（而非传统的样本内误差）进行协方差矩阵估计和预测协调。其次，我们引入了基于决策的聚合层次用于预测和协调，其中某些预测时域是基于实际所需的决策来确定的。第三，我们评估模型的预测性能时，不仅关注其最小化误差的能力，还关注其在降低决策成本（如辅助服务中的惩罚）方面的有效性。我们的结果表明，基于统计的层次结构倾向于采用较不保守的预测，从而减少收入损失。另一方面，基于决策的协调方法在精度和决策成本之间提供了更为平衡的折衷，使其在实际应用中更具吸引力。