English(EN) Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure

新方法预测不断变化的充电基础设施中的电动汽车充电需求

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一种新的电动汽车（EV）充电需求预测方法，这对于管理电动汽车对电力系统的影响至关重要。该方法利用了苏格兰公共充电站的大型数据集，这些充电站具有分层结构并会持续进行基础设施变更。通过将分层预测与为缺失和时变数据设计的保形预测技术相结合，CP-MNR-Nested 算法可在分层的所有级别提供有效的预测区间。 AI

影响这项研究通过提供更准确的电动汽车充电模式预测，有望改善电网管理和灵活性。

排序理由该集群包含一篇学术论文，详细介绍了一种预测电动汽车充电需求的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Guillaume Principato, Yvenn Amara-Ouali, Yannig Goude, Bachir Hamrouche, Jean-Michel Poggi · 2026-10-12 04:00

时变分层基础设施中电动汽车充电的概率预测

arXiv:2610.13588v1 Announce Type: cross Abstract: Uncertainty quantification for electric vehicle (EV) charging demand is essential to turn the increasing pressure of EV adoption on power systems into a source of flexibility. We study a large-scale dataset of public charging stat…

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时变分层基础设施中电动汽车充电的概率预测

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