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. The proposed methodology projects 15 meteorological and geometric variables into a Koopman-linearized Riemannian manifold to systematically map complex climatic dynamics. The architecture integrates a Spectral Calibration unit and a multiplicative Thermodynamic Alpha-Gate. This system synthesizes real-time atmospheric opacity with theoretical clear-sky boundary models, structurally enforcing strict celestial geometry compliance. This completely neutralizes phantom nocturnal generation while maintaining zero-lag synchronization during rapid weather shifts. Validated against a rigorous five-year testing horizon in a severe semi-arid climate, the framework achieves an RMSE of 18.31 Wh/m2 and a Pearson correlation of 0.988. The model strictly maintains a zero-magnitude nocturnal error across all 1826 testing days and exhibits a sub-30-minute phase response during high-frequency transients. Comprising exactly 63,458 trainable parameters, this ultra-lightweight design establishes a robust, thermodynamically consistent standard for edge-deployable microgrid controllers.

翻译：自主离网光伏系统的稳定运行依赖于遵循大气热力学的太阳预测算法。当代深度学习模型持续表现出关键异常，主要体现在云瞬变期间的严重时相滞后以及物理上不可能的夜间发电现象。为解决数据驱动建模与确定性天体力学之间的这种分歧，本研究提出了热力学流体流形网络。该方法将15个气象与几何变量投影至Koopman线性化黎曼流形，以系统化映射复杂的气候动力学过程。该架构集成了频谱校准单元与乘法性热力学阿尔法门。该系统将实时大气透明度与理论晴空边界模型相结合，从结构上强制执行严格的天体几何约束，从而完全消除虚假的夜间发电，同时在快速天气变化期间保持零延迟同步。在严酷半干旱气候条件下经过严格的五年测试期验证，该框架实现了18.31 Wh/m²的均方根误差与0.988的皮尔逊相关系数。该模型在所有1826个测试日中严格维持夜间误差为零，并在高频瞬变期间呈现低于30分钟的相位响应。该超轻量级设计仅包含63,458个可训练参数，为可边缘部署的微电网控制器建立了鲁棒的热力学一致标准。