The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management. Accurately predicting DER adoption is critical for proactive infrastructure planning, but the inherent uncertainty and spatial disparity of DER growth complicate traditional forecasting approaches. Moreover, the hierarchical structure of distribution grids demands that predictions satisfy statistical guarantees at both the circuit and substation levels, a non-trivial requirement for reliable decision-making. In this paper, we propose a novel uncertainty quantification framework for DER adoption predictions that ensures validity across hierarchical grid structures. Leveraging a multivariate Hawkes process to model DER adoption dynamics and a tailored split conformal prediction algorithm, we introduce a new nonconformity score that preserves statistical guarantees under aggregation while maintaining prediction efficiency. We establish theoretical validity under mild conditions and demonstrate through empirical evaluation on customer-level solar panel installation data from Indianapolis, Indiana that our method consistently outperforms existing baselines in both predictive accuracy and uncertainty calibration.

翻译：分布式能源资源的快速增长为电网管理带来了机遇与运行挑战。准确预测分布式能源资源采纳对主动式基础设施规划至关重要，但分布式能源资源增长固有的不确定性和空间差异性使传统预测方法面临困境。此外，配电网的分层结构要求预测结果在馈线和变电站层面均满足统计保证，这对于可靠决策而言是一项非平凡要求。本文提出了一种新颖的不确定性量化框架，用于分布式能源资源采纳预测，确保在跨层次电网结构中保持有效性。通过采用多变量霍克斯过程建模分布式能源资源采纳动态，并结合定制化分割保形预测算法，我们引入了一种新的不一致性评分函数，该函数在聚合条件下保持统计保证的同时维持预测效率。我们在温和条件下建立了理论有效性，并基于印第安纳州印第安纳波利斯市的用户级太阳能板安装数据进行的实证评估表明，我们的方法在预测准确性和不确定性校准方面持续优于现有基线方法。