통계분석을 통한 주거용 건물의 주택유형별 에너지 예측모델 개발 (Development of Energy Prediction Model for Residential Buildings through Statistical Analysis)

서지정보

· 발행기관 : 한국태양에너지학회
· 수록지 정보 : 한국태양에너지학회 논문집 / 40권 / 6호 / 51 ~ 60페이지
· 저자명 : 김지원, 곽영훈, 허정호

초록

Reducing building energy is necessary to realize the post-2020 national greenhouse gas reduction target (37% reduction from the BAU). Among them, residential buildings account for about 64% of buildings and must seek ways to save energy. To achieve this, it is necessary to analyze the factors impacting energy consumption. Therefore, this study conducted a statistical analysis to build an energy prediction model for residential buildings by utilizing the “Furniture Energy Permanent Sample Survey” microdata provided by the Korea Energy Economics Institute. Energy consumption, a dependent variable, was a summed up value of annual electricity, city gas, and district heating consumption, and the factors influencing energy consumption were selected as variables, considering prior research and multiple recovery analysis results of microdata. Subsequently, an analysis of variance was performed to verify the significance of the regression equation. Insignificant variables were eliminated by the statistical analysis results, and regression models were presented for the remaining variables. Therefore, the regression model derived from this study is expected to be the basis for the prediction and reduction of energy consumption, in the future, for residential household characteristics.

영어초록

Reducing building energy is necessary to realize the post-2020 national greenhouse gas reduction target (37% reduction from the BAU). Among them, residential buildings account for about 64% of buildings and must seek ways to save energy. To achieve this, it is necessary to analyze the factors impacting energy consumption. Therefore, this study conducted a statistical analysis to build an energy prediction model for residential buildings by utilizing the “Furniture Energy Permanent Sample Survey” microdata provided by the Korea Energy Economics Institute. Energy consumption, a dependent variable, was a summed up value of annual electricity, city gas, and district heating consumption, and the factors influencing energy consumption were selected as variables, considering prior research and multiple recovery analysis results of microdata. Subsequently, an analysis of variance was performed to verify the significance of the regression equation. Insignificant variables were eliminated by the statistical analysis results, and regression models were presented for the remaining variables. Therefore, the regression model derived from this study is expected to be the basis for the prediction and reduction of energy consumption, in the future, for residential household characteristics.

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