发表机构
KIST Medical College and Teaching Hospital; OTH Regensburg（KIST医学院教学医院; 雷根斯堡应用技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对德国再调度数据延迟与分布偏移，比较多种概率模型，发现滚动校准的LightGBM预测最优，但极端拥堵时覆盖率不足。
AI 中文摘要
公开的再调度记录为电网拥堵预测提供了经验数据，但延迟报告、零膨胀分布和时间偏移构成了主要建模挑战。我们在实验性强加的信息年龄约束下，使用已发布的德国输电记录评估概率性机器学习预测的准确性和可靠性。该基准评估了2021年至2024年间四个德国输电系统运营商的八组每日上行和下行干预能量序列（共48,242条合格记录；2024年有354个评估日期）。我们在最小七天目标延迟约束下，比较了季节性经验模型、正则化自回归（ARX）模型、分位数LightGBM、GRU和Transformer模型。神经架构使用零审查输出头以适应精确为零的结果。使用归一化加权区间分数（nWIS）、经验覆盖率和块自助法推断评估了静态、滚动和自适应延迟反馈校准方法。原始LightGBM实现了nWIS 0.7952，分别优于ARX（1.0604）和季节性基线（0.8739）25.0%和9.0%（Holm调整后p<0.005）。滚动校准将LightGBM的nWIS提升至0.7767，而静态校准为0.8251（p=0.0092），名义90%区间的覆盖率为91.81%。零审查Transformer实现了nWIS 0.8161，与LightGBM无显著差异（p=0.260）。然而，总体覆盖率掩盖了高量干预期间的显著欠覆盖（高于阈值事件中的覆盖率为61.91%）。这些结果表明，具有滚动校准的提升树模型在目标延迟下提供了对总体再调度量的准确概率预测，而名义总体有效性并不能确保极端拥堵事件期间的可靠性。
英文摘要
Public redispatch records provide empirical data for grid congestion forecasting, but delayed reporting, zero-inflated distributions, and temporal shift present major modeling challenges. We assess the accuracy and reliability of probabilistic machine-learning forecasts using published German transmission records under experimentally imposed information-age constraints. The benchmark evaluates eight daily series of upward and downward intervention energy across four German transmission system operators from 2021 to 2024 (48,242 eligible records; 354 evaluation dates in 2024). We compare seasonal empirical, regularized autoregressive (ARX), quantile LightGBM, GRU, and Transformer models under a minimum seven-day target-latency constraint. Neural architectures use a zero-censored output head to accommodate exact-zero outcomes. Static, rolling, and adaptive delayed-feedback calibration are evaluated using normalized weighted interval score (nWIS), empirical coverage, and block-bootstrap inference. Raw LightGBM achieved nWIS 0.7952, outperforming ARX (1.0604) and the seasonal baseline (0.8739) by 25.0% and 9.0%, respectively (Holm-adjusted p<0.005). Rolling calibration improved LightGBM to nWIS 0.7767 versus 0.8251 for static calibration (p=0.0092), with 91.81% coverage for nominal 90% intervals. The zero-censored Transformer achieved nWIS 0.8161, with no significant difference from LightGBM (p=0.260). However, aggregate coverage concealed substantial undercoverage during high-volume interventions (61.91% coverage among above-threshold events). These results show that boosted-tree models with rolling calibration provide accurate probabilistic forecasts of aggregate redispatch volumes under target delays, while nominal aggregate validity does not ensure reliability during extreme congestion events.
Comments15 pages, 4 figures, 3 tables. Code available at https://github.com/faraz-shamim/german-redispatch-ml
