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논문 기본 정보

자료유형
학술대회자료
저자정보
Xiguan Liang (Sungkyunkwan University) Owen Anderton (Sungkyunkwan University) Sowoo Park (Sungkyunkwan University) Doosam Song (Sungkyunkwan University)
저널정보
대한설비공학회 대한설비공학회 학술발표대회논문집 대한설비공학회 2022년도 동계학술발표대회 논문집
발행연도
2022.11
수록면
72 - 77 (6page)

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초록· 키워드

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Indoor occupancy estimation is of importance to control the indoor environment with energy efficiency and safe from infectious virus such as SARS-CoV2. Traditional methods of counting number of people, such as image recognition from webcams, can cause expensive initial installation costs and raise the issue of personal privacy invasion. To accurately detect real-time occupancy rates and avoid the human rights violation, a low-cost occupancy prediction data-driven method based on indoor CO₂ concentrations is proposed in this study. Field measurements were taken in one classroom at a university, occupancy image data were recorded with the consent of the subjects, and indoor and outdoor CO₂ concentrations, temperature and humidity data were recorded. Occupancy rate estimation model based on CO₂ concentration was created through machine learning. The final 4 models (Linear Regression, Multilayer Perception, Random Forest, and Gradient Boosting Regressor) validation was performed using CO₂ concentration data and real-time occupancy data for 1 additional working day for this classroom. The results show that the Gradient Boosting Regressor (GBR) model for occupancy prediction based on CO₂ concentration can accurately derive the relationship between indoor CO₂ concentrations and the number of people and can effectively predict short-term changes in occupancy rate.

목차

Abstract
1 Introduction
2 Methodology
3 Results and discussion
4 Conclusion
References

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