The rapid deployment of distributed energy resources (DER) has introduced significant spatio-temporal uncertainties in power grid management, necessitating accurate multilevel forecasting methods. However, existing approaches often produce overly conservative uncertainty intervals at individual spatial units and fail to properly capture uncertainties when aggregating predictions across different spatial scales. This paper presents a novel hierarchical spatio-temporal model based on the conformal prediction framework to address these challenges. Our approach generates circuit-level DER growth predictions and efficiently aggregates them to the substation level while maintaining statistical validity through a tailored non-conformity score. Applied to a decade of DER installation data from a local utility network, our method demonstrates superior performance over existing approaches, particularly in reducing prediction interval widths while maintaining coverage.

翻译：分布式能源资源的快速部署给电网管理带来了显著的时空不确定性，亟需精确的多层级预测方法。然而，现有方法通常在单个空间单元上产生过于保守的不确定性区间，且在跨不同空间尺度聚合预测时无法准确捕捉不确定性。本文提出一种基于共形预测框架的新型分层时空模型以应对这些挑战。该方法生成电路级DER增长预测，并通过定制的非共形评分有效将其聚合至变电站层级，同时保持统计有效性。应用于本地公用事业网络十年的DER安装数据，我们的方法展现出优于现有方法的性能，特别是在保持覆盖范围的同时有效缩减了预测区间宽度。