Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems
For off-grid photovoltaic systems requiring accurate, computationally efficient forecasting, PISSM provides a lightweight model that ensures physical consistency and prevents impossible predictions.
PISSM introduces a physics-informed state space model for solar irradiance forecasting that achieves superior accuracy with fewer than 40,000 parameters, enabling reliable real-time forecasting on edge-deployed microcontrollers for off-grid systems.
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. PISSM utilizes a dynamic Hankel matrix embedding to filter stochastic sensor noise by transforming raw meteorological sequences into a robust state space. A Linear State Space Model replaces heavy attention mechanisms, efficiently modeling temporal dependencies for parallel processing. Crucially, a novel Physics-Informed Gating mechanism leverages the Solar Zenith Angle and Clearness Index to structurally bound outputs, ensuring predictions strictly obey diurnal cycles and preventing nocturnal errors. Validated on a multi-year dataset for Omdurman, Sudan, PISSM achieves superior accuracy with fewer than 40,000 parameters, establishing an ultra-lightweight benchmark for real-time off-grid control.