A practical real-time control strategy for high-performance building HVAC operation concerning potential pandemic outbreaks in post-pandemic era
李炳绪,王盛卫,历秀明
摘要:
The lessons learned from the COVID-19 pandemic emphasize the need for building HVAC systems to be adaptable and prepared to adjust their operation for infection risk mitigation for future pandemics. Most guidelines suggest increasing outdoor air fraction in HVAC systems during pandemics. However, this approach is not energy-efficient due to the conditioning of additional outdoor air, and many practical systems face limitations in introducing more outdoor air than normal due to capacity or design constraints. Therefore, there is an urgent need for an alternative solution for HVAC system operation during pandemics that is energy-efficient, can effectively mitigate infection risks and does not require extensive retrofits to existing systems. To meet this need, this study proposes a practical real-time control strategy for high-performance HVAC operation in response to potential pandemic outbreaks. First, a supply air temperature (SAT) reset scheme for infection risk mitigation is proposed based on Mamdani fuzzy inference theory. Then, a coordinated control scheme is proposed to coordinate SAT reset with chilled water temperature adjustment for energy-efficiency enhancement. This strategy does not require system equipment retrofits but only changes in control logic to transition to pandemic operation. The effectiveness of the proposed strategy is validated in a high-fidelity Modelica-based simulation environment under various weather conditions. Results show that, compared to normal operation, the proposed strategy can significantly reduce new infection cases by 32~46% without increasing energy use, and may even achieve slight energy savings of 9~13%, while maintaining expected occupant comfort.
HVAC system 暖通空调系统
real-time control 实时控制
infection risk mitigation 传染风险削减
energy efficiency 能效
post-pandemic era 后疫情时代
论文绘图:
Figure 9. 策略1-3在3天模拟期(7月8日至7月10日)的比较结果。
Figure 12. 不同控制策略下三个测试期内各来源ECA的每日分解结果比较。
Figure 14. 不同策略下三个测试期内区域温度(a–c)和相对湿度(d–f)的概率密度分布。
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