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English(EN) Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models

新的STOIC框架通过基础模型增强能源预测 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了STOIC，一个用于能源需求预测的新颖框架，该框架将时空图神经网络（STGNNs）与基础模型相结合。该方法旨在提供比传统的点预测更可靠的不确定性估计，这对于电网稳定性和运营规划至关重要。STOIC将预测残差重新格式化为表格形式，以便与基础模型进行上下文学习，从而无需重新训练即可进行校准，并有效捕获复杂时空依赖性。 AI

影响 增强了能源系统中的不确定性量化，可能改善电网稳定性和运营规划。

排序理由 该集群包含一篇学术论文，详细介绍了一种使用基础模型和图神经网络进行能源时间序列预测的新方法。

在 arXiv cs.LG 阅读 →

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新的STOIC框架通过基础模型增强能源预测 · 跟踪2个来源

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该集群包含一篇学术论文，详细介绍了一种使用基础模型和图神经网络进行能源时间序列预测的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Keivan Faghih Niresi, Alice Cicirello, Olga Fink ·

    基于基础模型的图上能量时间序列的关系与序列一致性推断

    arXiv:2606.31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have recently achieved strong performance in point forecas…

  2. arXiv cs.LG TIER_1 English(EN) · Olga Fink ·

    基于基础模型的图上能量时间序列的关系与序列一致性推断

    Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have recently achieved strong performance in point forecasting by jointly modeling temporal dynamics and r…

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