🤖 AI Summary
This study addresses critical limitations in deep learning–based solar irradiance forecasting for off-grid photovoltaic systems, particularly spurious nighttime generation and phase lag during abrupt cloud transitions. To overcome these issues, the authors propose a lightweight prediction framework that integrates physical priors by embedding 22-dimensional meteorological and celestial geometric variables into a Koopman-linearized Riemannian manifold. The architecture incorporates a spectral calibration unit and a thermodynamic Alpha gating mechanism to explicitly encode atmospheric opacity and clear-sky boundary constraints. This approach achieves the first explicit modeling of celestial mechanics and thermodynamic principles within a compact neural network, entirely eliminating nighttime prediction errors (zero bias over 1,826 days) and attaining sub-30-minute response latency under rapidly changing weather conditions. Evaluated over five years in a semi-arid climate, the model achieves an RMSE of 18.31 Wh/m² and a Pearson correlation coefficient of 0.988 with only 63,458 parameters.
📝 Abstract
The stable operation of autonomous off-grid photovoltaic systems requires 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 methodology projects 22 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 optical transients. Comprising exactly 63,458 trainable parameters, this ultra-lightweight design establishes a robust, thermodynamically consistent standard for edge-deployable microgrid controllers.