VB-NET: A physics-constrained gray-box deep learning framework for modeling air conditioning systems as virtual batteries
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914378662543360 |
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| author | Qi, Yuchen Guo, Ye Xu, Yinliang |
| author_facet | Qi, Yuchen Guo, Ye Xu, Yinliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07601 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VB-NET: A physics-constrained gray-box deep learning framework for modeling air conditioning systems as virtual batteries Qi, Yuchen Guo, Ye Xu, Yinliang Systems and Control 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. |
| title | VB-NET: A physics-constrained gray-box deep learning framework for modeling air conditioning systems as virtual batteries |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.07601 |