为分布式能源接入预测提供分层概率置信保障，兼顾准确性与统计可靠性。
Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption
- 用多变量霍克斯过程建模能源接入动态，结合定制分裂置信预测算法
- 在印第安纳波利斯客户级数据上，预测精度和置信度校准均优于现有方法
- 适用于电网规划者需在电路与变电站层级同时保证预测可靠性的场景
分布式能源（DER）的快速增长给电网管理带来了机遇与挑战。准确预测其采纳情况对主动基础设施规划至关重要，但其固有的不确定性与空间差异性使传统预测方法难以应对。此外，配电网络的层级结构要求预测结果在电路和变电站层面均满足统计保证，这对可靠决策构成非平凡挑战。本文提出一种新型不确定性量化框架，确保在分层电网结构中预测的有效性。通过多变量霍克斯过程建模DER采纳动态，并设计一种定制化的分裂置信预测算法，引入新的非符合度评分，在聚合时仍保持统计保证的同时提升预测效率。理论证明在弱条件下具有有效性，并基于印第安纳波利斯客户级太阳能安装数据的实证评估显示，该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。
原文摘要 · Abstract (English)
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.
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