Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems
Summary: arXiv:2604.11807v2 Announce Type: replace-cross
Abstract
The stable operation of off-grid photovoltaic systems requires accurate, computationally efficient solar forecasting. Contemporary deep learning models often suffer from massive computational overhead and physical blindness, generating impossible predictions. This paper introduces the Physics-Informed State Space Model (PISSM) to bridge the gap between efficiency and physical accuracy for edge-deployed microcontrollers.
Introduction
With the increasing reliance on renewable energy sources, particularly solar power, the demand for reliable forecasting methods has never been more critical. Off-grid photovoltaic systems, which operate independently of traditional power grids, face unique challenges in forecasting solar irradiance. The accuracy of these forecasts directly impacts the efficiency and stability of energy systems.
Methodology
The PISSM leverages a dynamic Hankel matrix embedding to filter out stochastic sensor noise, transforming raw meteorological sequences into a robust state space. This transformation is essential for modeling the inherent uncertainties in solar irradiance data. Instead of employing heavy attention mechanisms, which are common in deep learning approaches, PISSM utilizes a Linear State Space Model that efficiently captures temporal dependencies.
Innovative Features
One of the notable advancements within PISSM is the implementation of a novel Physics-Informed Gating mechanism. This mechanism effectively employs two critical parameters:
- Solar Zenith Angle: This parameter provides information about the sun’s position in the sky, which is essential for accurate solar predictions.
- Clearness Index: This index quantifies the transparency of the atmosphere, influencing the amount of solar radiation reaching the earth’s surface.
By using these parameters, the gating mechanism structurally bounds the model’s outputs, ensuring that predictions adhere strictly to diurnal cycles. This feature is pivotal in preventing erroneous nocturnal predictions, a common issue in traditional forecasting methods.
Results and Validation
The performance of PISSM has been validated on a multi-year dataset for Omdurman, Sudan. The results indicate that PISSM achieves superior accuracy compared to existing models while maintaining a lightweight architecture, consisting of fewer than 40,000 parameters. This efficiency establishes PISSM as a benchmark for real-time control in off-grid applications.
Conclusion
The introduction of Physics-Informed State Space Models marks a significant advancement in solar irradiance forecasting for off-grid systems. By effectively bridging the gap between computational efficiency and physical accuracy, PISSM offers a promising solution for enhancing the reliability of renewable energy systems. As the demand for sustainable energy solutions continues to grow, innovations like PISSM will play a crucial role in shaping the future of energy management.