개방형 공공데이터를 활용한 영유아 시설의 에너지 소비 성능 평가를 위한 회귀 모델 개발
Development of a Regression Model for Evaluating Energy Consumption Performance of Daycare Centers Using Open Public Data
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To achieve carbon neutrality and address climate change, policies such as zero-energy buildings (ZEB) and green remodeling (GR) are being implemented globally to reduce building carbon emissions. However, the current energy use intensity (EUI) metric may not adequately reflect the operational characteristics. Therefore, a standardized model is necessary to evaluate building energy consumption accurately. This study developed a regression model using open public data to evaluate the energy performance of daycare centers. Key influencing factors, such as nursery room area, number of children, building area, floor area ratio, calculated gross floor area, and height, were identified for correlation analysis. The model predicts cooling, heating, and base energy usage, with R² values ranging from 0.4147 to 0.6520. Future work will focus on enhancing the credibility of the model by incorporating building performance and external environmental variables to infer and evaluate the impact of user behavior.
Abstract
To achieve carbon neutrality and address climate change, policies such as zero-energy buildings (ZEB) and green remodeling (GR) are being implemented globally to reduce building carbon emissions. However, the current energy use intensity (EUI) metric may not adequately reflect the operational characteristics. Therefore, a standardized model is necessary to evaluate building energy consumption accurately. This study developed a regression model using open public data to evaluate the energy performance of daycare centers. Key influencing factors, such as nursery room area, number of children, building area, floor area ratio, calculated gross floor area, and height, were identified for correlation analysis. The model predicts cooling, heating, and base energy usage, with R² values ranging from 0.4147 to 0.6520. Future work will focus on enhancing the credibility of the model by incorporating building performance and external environmental variables to infer and evaluate the impact of user behavior.
참고문헌 (15)
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