English(EN) Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting

物理信息状态空间模型提高太阳能发电预测精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新颖的物理信息状态空间框架PowerMamba，旨在实现精确的日度光伏发电预测。该方法将可微分光伏转换模型与状态空间骨干相结合，从数值天气预报输入中学习残差校正。实验表明，与纯物理基线相比，该方法在预测精度方面有了显著提高，RMSE降低了38.5%，MAE降低了45.8%，同时通过确保预测保持在物理可行性范围内，增强了鲁棒性。 AI

影响提高了可再生能源预测的准确性和鲁棒性，有望改善电网稳定性和效率。

排序理由该集群包含一篇详细介绍新模型及其实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Phil Aupke, Muhammad Hassan, Khalid Ali, Seema, Andreas Kassler, Andreas Theocharis · 2026-10-15 04:00

面向日间光伏发电预测的物理信息状态空间模型

arXiv:2610.14014v1 Announce Type: cross Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely data-driven models often ignore the underlying PV conversion process and may pro…

本文如何被排名

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面向日间光伏发电预测的物理信息状态空间模型

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