🤖 AI Summary
This study addresses phase lag during rapid cloud transients and non-physical nighttime power predictions in off-grid photovoltaic systems by proposing a thermodynamic liquid manifold network architecture. The method embeds 15-dimensional meteorological and geometric variables into a Koopman-linearized Riemannian manifold, integrating a spectral calibration unit with a thermodynamic Alpha gating mechanism. By incorporating real-time atmospheric opacity and a theoretical clear-sky boundary model, the architecture structurally enforces celestial geometric constraints. Evaluated on five years of semi-arid climate data, the model achieves an RMSE of 18.31 Wh/m² and a Pearson correlation coefficient of 0.988, maintains zero prediction error over 1,826 nights, exhibits response delays under 30 minutes during abrupt weather changes, and contains only 63,458 parameters—demonstrating markedly improved physical consistency and dynamic response accuracy.
📝 Abstract
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.