Thermodynamic Liquid Manifold Networks: Physics-Bounded Deep Learning for Solar Forecasting in Autonomous Off-Grid Microgrids
Summary: arXiv:2604.11909v1 Announce Type: cross
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.
Innovative Methodology
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.
Key Features
- Phantom Nocturnal Generation Neutralization: The system effectively eliminates the occurrence of phantom nocturnal power generation, which is a common issue in traditional models.
- Zero-Lag Synchronization: Maintains zero-lag synchronization during rapid weather shifts, allowing for more accurate forecasting.
- Validation: The framework has been validated against a rigorous five-year testing horizon in a severe semi-arid climate.
- Performance Metrics: Achieves an RMSE of 18.31 Wh/m2 and a Pearson correlation of 0.988, demonstrating high accuracy.
- Nocturnal Error Control: The model strictly maintains a zero-magnitude nocturnal error across all 1826 testing days.
- Rapid Response: Exhibits a sub-30-minute phase response during high-frequency optical transients, crucial for adaptive microgrid operations.
- Efficient Design: Comprising exactly 63,458 trainable parameters, this ultra-lightweight design is suitable for edge-deployable microgrid controllers.
Conclusion
The introduction of the Thermodynamic Liquid Manifold Network marks a significant advancement in solar forecasting for autonomous off-grid microgrids. By addressing critical issues faced by contemporary deep learning models and integrating physical principles of atmospheric thermodynamics, this research sets a new standard for the development of robust, efficient, and reliable solar forecasting systems. The findings reinforce the importance of aligning machine learning approaches with fundamental physical laws to enhance the performance of renewable energy systems.