English(EN) Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers

新型 Transformer 模型提升电力中断恢复时间预测精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种纵向表格 Transformer (LTT) 模型，以提高电力中断预计恢复时间 (ETR) 的准确性。与将 ETR 视为静态的先前方法不同，LTT 模型利用与中断相关的修订和更新序列来提供更精细的估计。在对超过 240,000 次中断的测试中，与公用事业公司发布的 ETR 和强大的基线相比，LTT 模型显著降低了不对称误差，同时还提高了所有测试公司的均方根误差。 AI

影响通过提供更准确的恢复时间估计，该模型可以提高公用事业公司在停电期间的客户满意度和决策能力。

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arXiv cs.AI TIER_1 English(EN) · Bogireddy Sai Prasanna Teja, Valliappan Muthukaruppan, Carls Benjamin · 2026-09-11 04:00

使用纵向表格 Transformer 预测电力中断的预计恢复时间

arXiv:2505.00225v2 Announce Type: replace-cross Abstract: Utilities publish Estimated Times of Restoration (ETRs) for customer-facing storm outages, and their accuracy governs whether customers can make sound decisions about food, medical equipment, and relocation. Prior work tre…

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使用纵向表格 Transformer 预测电力中断的预计恢复时间

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