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 summarizes an arXiv paper (2604.11807) by Mohammed Ezzaldin Babiker Abdullah on solar irradiance forecasting for autonomous off-grid photovoltaic systems. The author argues that existing deep learning forecasters suffer from two critical anomalies: severe temporal phase lags during cloud transients and physically impossible nocturnal power generation predictions. The proposed solution, a Thermodynamic Liquid Manifold Network, projects 15 meteorological and geometric variables onto a Koopman-linearized Riemannian manifold. It incorporates a spectral calibration unit and a thermodynamic Alpha gate that combines real-time atmospheric opacity with a theoretical clear-sky boundary model, reportedly eliminating nocturnal ghost power generation while maintaining zero-lag synchronization during high-frequency transients. The model is notably compact, containing only 63,458 trainable parameters. The work spans machine learning, AI, and systems engineering (cs.LG, cs.AI, eess.SY).

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

Paper Overview

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

Abstract (Translated Summary)

The stable operation of autonomous off-grid photovoltaic systems requires solar irradiance forecasting algorithms that respect atmospheric thermodynamics. Contemporary deep learning models exhibit critical anomalies: severe temporal phase lags during cloud transients and physically impossible nocturnal power generation predictions.

The paper introduces the Thermodynamic Liquid Manifold Network, which:

Projects 15 meteorological and geometric variables onto a Koopman-linearized Riemannian manifold
Integrates a spectral calibration unit and a thermodynamic Alpha gate
Combines real-time atmospheric opacity with a theoretical clear-sky boundary model
Completely eliminates nocturnal ghost power generation while maintaining zero-lag synchronization during high-frequency transients
Uses only 63,458 trainable parameters

Original Abstract

> 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.

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