VB-NET: A physics-constrained gray-box deep learning framework for modeling air conditioning systems as virtual batteries

Abstract

The increasing penetration of renewable energy necessitates unlocking demand-side flexibility. While air conditioning (AC) systems offer significant thermal inertia, existing physical and data-driven models struggle with parameter acquisition, interpretability, and data scarcity. This paper proposes VB-NET, a physics-constrained gray-box deep learning framework that transforms complex AC thermodynamics into a standardized Virtual Battery (VB) model. We first mathematically prove the isomorphic equivalence between the AC and VB models. Subsequently, VB-NET is designed to strictly enforces physical laws by decoupling shared meteorological drivers from private building thermal fingerprints and embedding a differentiable physics layer. Experimental results demonstrate that VB-NET significantly outperforms conventional black-box models in state of charge tracking while successfully recovering underlying thermodynamic laws to yield physically consistent parameters. Furthermore, utilizing multi-task learning and terminal sensitivity modulation, VB-NET overcomes the cold-start dilemma, achieving high-precision modeling for new AC units using only 2% to 6% of historical data. Ultimately, this study provides an interpretable and data-efficient pathway for aggregating decentralized AC resources for grid regulation.