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办公建筑开放空间的 HVAC 系统控制的网络增强优化。

Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building.

机构信息

Mechanical Engineering, Texas A&M University, College Station, TX 77840, USA.

Engineering Technology & Industrial Distribution (Joint Appt with Mechanical Engineering), Texas A&M University, College Station, TX 77840, USA.

出版信息

Sensors (Basel). 2023 May 18;23(10):4857. doi: 10.3390/s23104857.

Abstract

Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates. Traditional comfort models also lack the ability to adapt to individual demands and sensations. This research developed a data-driven thermal comfort model to improve the overall thermal comfort of occupants in office buildings. An architecture based on cyber-physical system (CPS) is used to achieve these goals. A building simulation model is built to simulate multiple occupants' behaviors in an open-space office building. Results suggest that a hybrid model can accurately predict occupants' thermal comfort level with reasonable computing time. In addition, this model can improve occupants' thermal comfort by 43.41% to 69.93%, while energy consumption remains the same or is slightly reduced (1.01% to 3.63%). This strategy can potentially be implemented in real-world building automation systems with appropriate sensor placement in modern buildings.

摘要

热舒适性对幸福感和工作效率至关重要。人类的热舒适性主要由建筑物中的暖通空调(供暖、通风、空调)系统控制。然而,暖通空调系统中热舒适性的控制指标和测量通常过于简化,仅使用有限的参数,无法准确控制室内气候中的热舒适性。传统的舒适模型也缺乏适应个体需求和感觉的能力。本研究开发了一种数据驱动的热舒适模型,以提高办公楼内人员的整体热舒适度。基于信息物理系统(CPS)的架构用于实现这些目标。建立了一个建筑模拟模型,以模拟开放式办公大楼中多个人员的行为。结果表明,混合模型可以在合理的计算时间内准确预测人员的热舒适水平。此外,该模型可以将人员的热舒适度提高 43.41%至 69.93%,同时保持能源消耗不变或略有降低(1.01%至 3.63%)。这种策略可以通过在现代建筑中适当放置传感器,在实际的建筑自动化系统中实施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/77ab/10223385/82c40331cbab/sensors-23-04857-g0A1.jpg

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