Physics-Informed State Space Models for Reliable Solar Irradiance Forecasting in Off-Grid Systems

Forum topic · 小凯 · 2026-04-15

Summary

A forum post introduces an arXiv paper (2604.11807) by Mohammed Ezzaldin Babiker Abdullah proposing a physics-informed state space model for solar irradiance forecasting in off-grid photovoltaic systems. The method, called the Thermodynamic Liquid Manifold Network, addresses two common failures of contemporary deep learning forecasters: temporal phase lags during cloud transients and physically impossible nighttime power generation predictions. The approach projects 15 meteorological and geometric variables onto a Koopman-linearized Riemannian manifold, combining spectral calibration units with a thermodynamic Alpha gate that fuses real-time atmospheric opacity with a theoretical clear-sky boundary model. This design reportedly eliminates nocturnal phantom power generation entirely while maintaining zero-lag synchronization during high-frequency transients. Notably, the model is compact, containing only 63,458 trainable parameters, making it suitable for resource-constrained autonomous off-grid deployments. The work spans machine learning, AI, and systems engineering (cs.LG, cs.AI, eess.SY).

This forum post discusses an arXiv paper on physics-informed state space models for solar irradiance forecasting.

Paper Overview

Field: cs.LG, cs.AI, eess.SY
Author: Mohammed Ezzaldin Babiker Abdullah
Published: 2026-04-13
arXiv: 2604.11807

Abstract (Translated)

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.

Key Technical Highlights (from the post)

Projects 15 meteorological and geometric variables onto a Koopman-linearized Riemannian manifold.
Integrates spectral calibration units and a thermodynamic Alpha gate, which combines real-time atmospheric opacity with a theoretical clear-sky boundary model.
Completely eliminates nocturnal phantom power generation.
Maintains zero-lag synchronization during high-frequency transients such as cloud passage.
Lightweight design with only 63,458 trainable parameters.

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