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English(EN) NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling

新的NeuralBES模拟器在建筑能耗建模中平衡了准确性和可扩展性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了NeuralBES，这是一种新颖的可微分模拟器，专为可扩展的建筑能耗建模而设计。该系统旨在解决高保真物理模拟器（如EnergyPlus）与纯数据驱动模型之间的权衡问题。前者准确但速度慢，后者可扩展但缺乏物理基础。NeuralBES使用共享的神经网络编码器来参数化电阻-电容热模型，该编码器将建筑元数据映射到物理系数，从而实现准确且物理上一致的能耗预测。 AI

影响 这种新的模拟器可以实现对数百万栋建筑进行更具可扩展性和更准确的能耗预测。

排序理由 该集群包含一篇详细介绍新的建筑能耗模拟模型的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的NeuralBES模拟器在建筑能耗建模中平衡了准确性和可扩展性

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该集群包含一篇详细介绍新的建筑能耗模拟模型的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ting-Yu Dai, Takuya Kurihana, Wing Yee Au, Hon Yung Wong ·

    NeuralBES：一种可微分、感知控制的模拟器，用于可扩展的建筑能源建模

    arXiv:2610.10459v1 Announce Type: new Abstract: Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of heterogeneous buildings. Existing tools force a hard tradeoff: high-fidelity physics s…

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