Data-driven operator learning for energy-efficient building control

• URL: http://arxiv.org/abs/2504.21243v1
• Date: Wed, 30 Apr 2025 00:45:49 GMT
• Title: Data-driven operator learning for energy-efficient building control
• Authors: Yuexin Bian, Yuanyuan Shi,
• Abstract summary: We present a data-driven framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable energy-efficient building ventilation control.<n>We train a neural operator transformer to learn the mapping from building control actions to airflow field distributions using high-resolution CFD data.<n> Experimental results demonstrate that our approach achieves substantial energy savings compared to maximum airflow rate control, rule-based control, and data-driven control based on regional average CO2 predictions.
• Score: 2.3326951882644553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Energy-efficient ventilation control plays a vital role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations offer high-fidelity modeling of airflow for building HVAC design, their high computational cost makes them impractical for practical adoption in real-time building management system. In this work, we present a data-driven framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable energy-efficient building ventilation control. Our method jointly optimizes airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train a neural operator transformer to learn the mapping from building control actions to airflow field distributions using high-resolution CFD data. This learned operator enables a gradient-based control framework capable of optimal decision-making. Experimental results demonstrate that our approach achieves substantial energy savings compared to maximum airflow rate control, rule-based control, and data-driven control based on regional average CO2 predictions, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method for enabling safe and energy-efficient building management.

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