대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제40권3호
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  • Pages.247-254
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  • 2024
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  • 2733-6239(pISSN)
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  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
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동절기 발코니 창호 표면의 결로 발생 예측을 위한 가상센서 가능성 검토

Assessing the Potential of Virtual Sensors in Predicting Winter Balcony Window Surface Condensation

  • 김승빈 (영남대 일반대학원 건축학과) ;
  • 손유라 (영남대 일반대학원 건축학과) ;
  • 양정훈 (영남대 건축학부)
  • 투고 : 2024.01.11
  • 심사 : 2024.02.28
  • 발행 : 2024.03.30
⟨ 이전 논문 다음 논문 ⟩

초록

Persistent condensation in residential spaces can lead to structural damage and mold growth, posing health risks to occupants. While existing studies focus on reducing condensation, there's a gap in research on condensation prediction. This study aims to explore the feasibility of a virtual sensor for condensation prediction using machine learning and data from prior studies. A high-accuracy virtual sensor model was developed and verified using condensation measurement data. Data preprocessing and Pearson correlation analysis were conducted, and input variables were selected through ReliefF evaluation. Indoor and outdoor temperature and humidity were chosen as final input variables. A prediction model was crafted using classification learning algorithms: Decision Tree(DT), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Validation of the prediction model was performed using Confusion matrix, Accuracy, and F-1 score. The accuracy of the virtual sensor model was 97.1% for Decision Tree, 98.5% for SVM, and 98.6% for MLP. The developed model is expected to effectively prevent condensation in residential spaces susceptible to surface condensation. Future work will focus on integrating virtual sensors into existing ventilation and air conditioning systems for practical application in residential spaces.

키워드

과제정보

이 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임.(No.2022R1F1A1064179)

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