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English(EN) Vision Transformers for Estimating Irradiance Using Data Scarce Sky Images

视觉Transformer在数据稀疏条件下估算太阳辐照度

研究人员开发了一种新颖的双框架方法,使用视觉Transformer(ViTs)从天空图像估算散射水平辐照度(DHI),尤其是在数据稀疏的环境中。该方法整合了计算出的大气参数,并使用顺序管道预测全局水平辐照度(GHI)作为改进DHI估算的特征。使用Chilbolton天文台数据的实证验证表明,即使仅在单个数据月上进行训练,该方法也能实现与在完整数据集上训练的最先进ViTs相当的均方根误差(RMSE)值。 AI

影响 这项研究为数据受限环境下的太阳能资源评估提供了一种经济高效的解决方案,有可能提高光伏系统的性能和能源预测。

排序理由 这是一篇详细介绍使用AI估算太阳辐照度的新颖方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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视觉Transformer在数据稀疏条件下估算太阳辐照度

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这是一篇详细介绍使用AI估算太阳辐照度的新颖方法的学术论文。[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) · David Hamlyn, Sunny Chaudhary, Tasmiat Rahman ·

    Vision Transformers for Estimating Irradiance Using Data Scarce Sky Images

    arXiv:2610.30940v1 Announce Type: cross Abstract: Accurate estimation of diffuse horizontal irradiance (DHI) is critical for optimising photovoltaic system performance and energy forecasting yet remains challenging in regions lacking comprehensive ground-based instrumentation. Re…