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English(EN) Comparative review of hybrid forecasting models for short-term prediction of building thermal load

EMD-LSTM-Markov模型在建筑热负荷预测精度方面领先

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文综述了用于建筑热负荷短期预测的混合预测模型，比较了各种数据驱动技术。研究发现，经验模态分解-长短期记忆-马尔可夫（EMD-LSTM-Markov）模型在预测提前一天供暖和生活热水需求方面取得了最高的准确性。虽然该模型准确预测了局部电力峰值，但它低估了高功率浪涌和尖峰。其他评估的方法，如SVM-SA和RF-ISSA-LSTM，产生了更平滑的需求曲线，但峰值预测精度较低。 AI

影响 这项研究为提高建筑能源需求预测的AI驱动模型的准确性提供了见解。

排序理由 该条目是一篇学术论文，详细介绍了预测模型的比较综述。[lever_c_demoted from research: ic=1 ai=1.0]

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EMD-LSTM-Markov模型在建筑热负荷预测精度方面领先

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该条目是一篇学术论文，详细介绍了预测模型的比较综述。[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) · Nikolaos A. Efkarpidis, Despoina Kothona, Georgios C. Christoforidis ·

    用于建筑热负荷短期预测的混合预测模型比较评测

    arXiv:2610.06881v1 Announce Type: cross Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the assessment tackles the comparison of data-driven models enhanced with other sta…

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