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논문 기본 정보

자료유형
학술대회자료
저자정보
Yue Yuan (Sungkyunkwan University) Jisoo Shim (Sungkyunkwan University) Doosam Song (Sungkyunkwan University)
저널정보
대한설비공학회 대한설비공학회 학술발표대회논문집 대한설비공학회 2020년도 하계학술발표대회 논문집
발행연도
2020.6
수록면
776 - 782 (7page)

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초록· 키워드

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Zero Energy Building (ZEB) is mainly based on the German passive house standards and becoming mandatory in Korea. Even though the concept of ZEB aims for two goals: low or zero energy building and thermal comfort of the occupants, the recently constructed ZEB in Korea tends to prioritize energy saving. Thermal discomfort and overheating issues have been reported in ZEB. In this paper, the overheating problem of ZEB is discussed, and the reason of the overheating is analyzed. According to the BEMS data for indoor temperature of the analyzed ZEB, it was found that the analyzed ZEB had an overheating of 36.2% during the examined period in cooling season. In order to extend the analysis for whole cooling season, a reliable prediction model based on deep learning is proposed in this study to obtain indoor temperature and CO₂ density during May 1<SUP>st</SUP> to October 31<SUP>st</SUP>. Because of the indoor environmental data of the analyzed building was insufficient to examine for the whole cooling season. The accuracy of the prediction model can reach 98.9%. Though parametric analysis of entire cooling season, the main reason of the overheating for the analyzed ZEB is the heat gain by increased occupancy and followed by solar radiation, outdoor temperature, outdoor humidity and wind speed. But in a condition of low occupancy level, solar radiation also has a high possibility to cause indoor overheating.

목차

Abstract
1. Introduction
2. Developing model for predicting indoor temperature and CO₂ density
3. Result and discussion
4. Conclusion
References

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