Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in 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 paralle...
| Authors | Mohammed Ezzaldin Babiker Abdullah |
|---|---|
| Primary category | cs.LG |
| Published | 13 April 2026 |
| Last updated at source | 17 April 2026 |
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