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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.16283 |
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| _version_ | 1866918301276307456 |
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| author | Jiang, Zixin Song, Ruizhi Li, Guowen Zhang, Yuhang O'Neill, Zheng Wang, Xuezheng Goldfeder, Judah Dong, Bing |
| author_facet | Jiang, Zixin Song, Ruizhi Li, Guowen Zhang, Yuhang O'Neill, Zheng Wang, Xuezheng Goldfeder, Judah Dong, Bing |
| contents | Modern buildings are increasingly interconnected with occupancy, heating, ventilation, and air-conditioning (HVAC) systems, distributed energy resources (DERs), and power grids. Modeling, control, and optimization of such multi-domain systems play a critical role in achieving building-sector decarbonization. However, most existing tools lack scalability and physical consistency for addressing these complex, multi-scale ecosystem problems. To bridge this gap, this study presents BESTOpt, a modular, physics-informed machine learning (PIML) framework that unifies building applications, including benchmarking, evaluation, diagnostics, control, optimization, and performance simulation. The framework adopts a cluster-domain-system/building-component hierarchy and a standardized state-action-disturbance-observation data typology. By embedding physics priors into data-driven modules, BESTOpt improves model accuracy and physical consistency under unseen conditions. Case studies on single-building and cluster scenarios demonstrate its capability for multi-level centralized and decentralized control. Looking ahead, BESTOpt lays the foundation for an open, extensible platform that accelerates interdisciplinary research toward smart, resilient, and decarbonized building ecosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16283 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework Jiang, Zixin Song, Ruizhi Li, Guowen Zhang, Yuhang O'Neill, Zheng Wang, Xuezheng Goldfeder, Judah Dong, Bing Systems and Control Modern buildings are increasingly interconnected with occupancy, heating, ventilation, and air-conditioning (HVAC) systems, distributed energy resources (DERs), and power grids. Modeling, control, and optimization of such multi-domain systems play a critical role in achieving building-sector decarbonization. However, most existing tools lack scalability and physical consistency for addressing these complex, multi-scale ecosystem problems. To bridge this gap, this study presents BESTOpt, a modular, physics-informed machine learning (PIML) framework that unifies building applications, including benchmarking, evaluation, diagnostics, control, optimization, and performance simulation. The framework adopts a cluster-domain-system/building-component hierarchy and a standardized state-action-disturbance-observation data typology. By embedding physics priors into data-driven modules, BESTOpt improves model accuracy and physical consistency under unseen conditions. Case studies on single-building and cluster scenarios demonstrate its capability for multi-level centralized and decentralized control. Looking ahead, BESTOpt lays the foundation for an open, extensible platform that accelerates interdisciplinary research toward smart, resilient, and decarbonized building ecosystems. |
| title | BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.16283 |