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English(EN) GateFormer: Forecast-Space Decomposition for Intra-Hour Solar Irradiance Prediction from Sky Images

GateFormer模型利用天空图像改进太阳辐照度预测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了GateFormer，一种新颖的双解码器Vision Transformer (ViT)模型，用于利用天空图像进行逐时太阳辐照度预测。该模型整合了观测到的全球水平辐照度 (GHI) 和视觉云动力学，将预测分解为轨迹预测和变化预测。在NREL-TSI基准测试中，GateFormer比现有的Transformer基线模型在RMSE上提高了9.6%，并在多个数据集上实现了最低的最终视线RMSE，比智能持久性预测提高了高达28.86%。自适应加权机制和监督校正进一步提高了其准确性，尤其是在正斜坡事件期间。 AI

影响 这项研究可能带来更准确的短期太阳能发电预测，从而提高电网稳定性和可再生能源整合。

排序理由 该条目描述了一篇关于太阳辐照度预测新模型的最新研究论文。[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 →

GateFormer模型利用天空图像改进太阳辐照度预测

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该条目描述了一篇关于太阳辐照度预测新模型的最新研究论文。[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, Jonathon Hare, Tasmiat Rahman ·

    GateFormer: Forecast-Space Decomposition for Intra-Hour Solar Irradiance Prediction from Sky Images

    arXiv:2610.30942v1 Announce Type: cross Abstract: Sub-hourly dispatch, balancing, and storage control increasingly depend on forecasts that anticipate rapid variability in solar generation. Accurate sky-image-based irradiance forecasting requires a combination of visual cloud dyn…

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