Natural Ventilation Control Strategies and Their Effectiveness in Different Climates
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Title
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Natural Ventilation Control Strategies and Their Effectiveness in Different Climates
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Description
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Natural ventilation (NV) is a sustainable building strategy that improves building energy efficiency, indoor thermal environment, and air quality. The successful implementation of natural ventilation relies on various factors, such as local climate, ambient air quality, floorplan, adjacent urban environment, window configuration, and urban noise. Among these factors, climate is the most influential one that determines the potential for natural ventilation, whereas the control of the heating, ventilation, and air conditioning (HVAC) system along with the window system becomes the most critical element for the successful natural ventilation in a given case, when only a few features are feasible to change.
This dissertation investigates global natural ventilation potential through NV hours and cooling energy saving percentage and estimates China’s natural ventilation potential by taking into account the additional factor of ambient air pollution. The aggregated energy savings and carbon reductions were estimated at the city level across 35 major Chinese cities. This dissertation then focuses on developing optimal NV control strategies to coordinate window operations with HVAC systems, aiming for an optimized synergy to achieve minimal energy consumption and maximum thermal comfort. A reinforcement learning control strategy is proposed, which demonstrates better performance compared to the rule-based heuristic control in accommodating stochastic internal heat gain, maintaining steady indoor thermal comfort, and reducing HVAC system operation. Finally, the effectiveness of different levels of automation in NV control is tested in a variety of distinct climates. Specifically, spontaneous occupant manual control, informed occupant manual control, and fully automatic control (including rule-based heuristic control and model predictive control [MPC]) are evaluated. The results demonstrated the superiority of fully automatic control with MPC, which significantly enhances building energy efficiency and thermal performance. The findings from this dissertation provide information for architects, building owners, and policymakers to realize the potential for natural ventilation in buildings.
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Doctor of Design
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Creator
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Chen, Yujiao
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Subject
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Natural ventilation
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Advanced building control
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Building energy efficiency
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MPC
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Reinforcement learning control
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Contributor
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Malkawi, Ali
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Samuelson, Holly
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Norford, Leslie
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Date
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2019-07-31T07:40:40Z
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2019-05
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2019-05-02
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2019
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2019-07-31T07:40:40Z
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Type
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Thesis or Dissertation
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text
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Format
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application/pdf
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application/pdf
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Identifier
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Chen, Yujiao. 2019. Natural Ventilation Control Strategies and Their Effectiveness in Different Climates. Doctoral dissertation, Harvard Graduate School of Design.
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:41021635
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Language
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en