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      KCI우수등재 SCOPUS

      공동주택 멀티 존 CO₂센서기반 환기제어 특성 연구

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      다국어 초록 (Multilingual Abstract)

      For decades, the primary method for improving indoor air quality has been to dilute the indoor contaminants. The CO2 concentration has been applied as a surrogate factor for indicating the other harmful contaminants through ventilation and air conditioning systems or air filtration systems. The ventilation requirements has been decided to maintain the perceived indoor air quality, It is not easy to control the ventilation system appropriately by manual operation. So, it is necessary to establish the strategies of ventilation control for healthy IAQ and energy conservation simultaneously in the apartment house. The spaces of residential apartment house consist of various kinds of individual room such as living room, bed rooms, kitchen etc. The ventilation system may designed to deliver the outside air to the individual room properly. These aspects has lead difficulty to appropriate control the ventilation system.
      In this study, multi-zone performance and mechanical ventilation system have been analyzed as a characteristics of DCV(Demand Control Ventilation) strategies with CO2 (Carbon Dioxide) sensor based. It is intended to analyze the control strategies of appropriate ventilation requirements. And also the reduction of energy consumption while improving indoor air quality. Finally, the optimal operating control of ventilation system has been discussed to improve indoor environment as applied for DCV with CO2 sensor networks. The results have been simulated for the perceived indoor air quality with CO2 sensor based DCV system. The strategies have been applied each occupant’s schedule in the multi-zone apartment house, and the characteristics of ventilation control based on CO2 concentrations and energy demands have been analyzed.
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      For decades, the primary method for improving indoor air quality has been to dilute the indoor contaminants. The CO2 concentration has been applied as a surrogate factor for indicating the other harmful contaminants through ventilation and air conditi...

      For decades, the primary method for improving indoor air quality has been to dilute the indoor contaminants. The CO2 concentration has been applied as a surrogate factor for indicating the other harmful contaminants through ventilation and air conditioning systems or air filtration systems. The ventilation requirements has been decided to maintain the perceived indoor air quality, It is not easy to control the ventilation system appropriately by manual operation. So, it is necessary to establish the strategies of ventilation control for healthy IAQ and energy conservation simultaneously in the apartment house. The spaces of residential apartment house consist of various kinds of individual room such as living room, bed rooms, kitchen etc. The ventilation system may designed to deliver the outside air to the individual room properly. These aspects has lead difficulty to appropriate control the ventilation system.
      In this study, multi-zone performance and mechanical ventilation system have been analyzed as a characteristics of DCV(Demand Control Ventilation) strategies with CO2 (Carbon Dioxide) sensor based. It is intended to analyze the control strategies of appropriate ventilation requirements. And also the reduction of energy consumption while improving indoor air quality. Finally, the optimal operating control of ventilation system has been discussed to improve indoor environment as applied for DCV with CO2 sensor networks. The results have been simulated for the perceived indoor air quality with CO2 sensor based DCV system. The strategies have been applied each occupant’s schedule in the multi-zone apartment house, and the characteristics of ventilation control based on CO2 concentrations and energy demands have been analyzed.

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      목차 (Table of Contents)

      • Abstract
      • 1. 서론
      • 2. CO₂-DCV관련 이론 고찰
      • 3. 기류 네트워크 시뮬레이션
      • 4. 기류시뮬레이션 결과
      • Abstract
      • 1. 서론
      • 2. CO₂-DCV관련 이론 고찰
      • 3. 기류 네트워크 시뮬레이션
      • 4. 기류시뮬레이션 결과
      • 5. 에너지 시뮬레이션
      • 6. 결론
      • 참고문헌
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