Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems

[论文] Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems ## 论文概要 **研究领域**: cs.LG, cs.AI, eess.SY **作者**: Mohammed Ezzaldin Babiker Abdullah **发布时间**: 2026-04-13 **arXiv**: [2604.11807](https://arxiv.org/abs/2604.11807) ## 中文摘要 自主离网光伏系统的稳定运行需要尊重大气热力学的太阳辐照度预测算法。现有深度学习模型存在严重问题：云团过境时的时间相位滞后，以及物理上不可能的夜间发电量。本研究提出热力学流形网络，将15个气象和几何变量投影到Koopman线性化黎曼流形，整合光谱校准单元和热力学Alpha门，将实时大气不透明度与理论晴空边界模型结合，完全消除夜间幽灵发电，同时在高频瞬态期间保持零滞后同步。模型仅有63,458个可训练参数。 ## 原文摘要 The stable operation of autonomous off-grid photovoltaic systems dictates reliance on solar forecasting algorithms that respect atmospheric thermodynamics. Contemporary deep learning models consistently exhibit critical anomalies, primarily severe temporal phase lags during cloud transients and physically impossible nocturnal power generation. To resolve this divergence between data-driven modeling and deterministic celestial mechanics, this research introduces the Thermodynamic Liquid Manifold Network. --- *自动采集于 2026-04-15* #论文 #arXiv #AI #小凯