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Energy Signature를 이용한 건물 에너지 사용량의 기후 정규화
유영서(Yoo, Young-Seo),이동혁(Yi, Dong-Hyuk),박철수(Park, Cheol-Soo) 대한건축학회 2021 대한건축학회 학술발표대회 논문집 Vol.41 No.2
Energy Use Intensity (EUI) has been widely used as a tool for building energy benchmarking. However, EUI can be different depending on climate even though two buildings’ thermal characteristics are identical to each other. In this regard, the authors propose a weather normalization process of EUI<SUP>*</SUP> called ‘energy signature’ using a non-linear relationship between outdoor monthly average air temperature and EUI. For this study, a medium office reference building developed by DOE was used for 76 different locations in South Korea. It is found that under various climate conditions EUI<SUP>*</SUP> can adequately reflect the building’s unique thermal characteristics, without being influenced by weather. In other words, the energy signature, or the weather-normalized EUI denoted by EUI<SUP>*</SUP> can be a good candidate for objective building energy benchmarking method.
건물 사용 시나리오에 따른 냉난방 부하 민감도 분석의 불확실성
유영서(Yoo, Young-Seo),이동혁(Yi, Dong-Hyuk),박철수(Park, Cheol-Soo) 대한건축학회 2021 대한건축학회논문집 Vol.37 No.11
It has been widely acknowledged that rational decision making at architectural design stage is important for energy efficient building design. In other words, the relationship between building energy use and design variables must be carefully analyzed. For this purpose, the global sensitivity analysis (GSA) can be a useful tool because GSA quantifies the unit change of a model’s output against the unit change of the individual model input for the entire input space. With the use of GSA, the influence of each design variable can be quantified in terms of sensitivity index. However, such sensitivity index can be dependent on crude assumptions for building usage scenarios, e.g. occupant density, equipment density, infiltration rate, etc. In general, these parameters are set as deterministic values based on simulationist’s subjective judgment, and it can be inferred that this subjective assmptions could cause uncertainty in sensitivity analysis. With this in mind, the authors propose a sensitivity analysis process for building energy design variables considering the uncertainty of building use scenarios. For this purpose, Sobol sensitivity analysis was performed on five design variables (wall U-value, fenestration SHGC, lighting power density, window U-value, window-wall ratio) according to assumptions of five building usage scenarios (occupant density, equipment density, infiltration rate, cooling and heating set-point temperatures). As a result, it is found that uncertainty in the sensitivity of design variables is significant and the sensitivity ranking of the design variables can vary. This indicates that in order to reach rational decision making, careful attention must be paid to selection of uncertain building usage scenarios, and stochastic sensitivity analysis must be employed.