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English(EN) Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building

TimesFM模型在大学建筑电力预测方面领先

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文探讨了各种机器学习模型在大学建筑日前电力预测中的有效性。该研究对传统的统计方法、XGBoost、循环神经网络和预训练的TimesFM模型进行了基准测试。TimesFM 3.0，特别是当结合天气协变量时，显示出最低的总体误差，与朴素的季节性预测相比，平均绝对误差降低了约43%。虽然XGBoost保持了竞争力并提供了更快的本地拟合，但TimesFM在无需进行建筑特定训练的情况下提供了一个强大的基准。 AI

影响 TimesFM有望成为能源预测的强大基准，可能提高建筑管理效率。

排序理由 研究论文发表在arXiv上，详细介绍了模型性能。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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TimesFM模型在大学建筑电力预测方面领先

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研究论文发表在arXiv上，详细介绍了模型性能。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Amey Vasulkar, Samuel Kernan Freire, Arie-Willem de Leeuw, Tadeo-Baldiri Salcedo-Rahola ·

    用于大学建筑日内电力预测的机器学习和时间序列基础模型

    arXiv:2610.12638v1 Announce Type: cross Abstract: Day-ahead electricity forecasts can help building operators schedule heating, ventilation, and other services around anticipated demand. Choosing a forecasting model also requires balancing accuracy with its forecast-time inputs, …

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