Segmented Linear Regression 모델 기반 상업용 에너지다소비건물의 전력 소비특성 분석

Abstract

The electricity energy consumption of commercial building is affected by thermal performance, building operation, occupancy schedule, and several weather parameters. Especially, it is difficult to characterize the end-used energy consumption of exist buildings with a single electricity meter for the whole building. Although many studies have been conducted to develop building energy baseline model to separate building base, heating, and cooling energy consumption from a total meter, the most model has been focused on monthly energy data with heating or cooling degree days and a specific building in general characteristics. This study presents an autonomous process with segmented linear regression model to analyze the characteristics of base, heating, and cooling energy consumption from total electricity meter for several highly energy demand commercial buildings by automatically searching best-fit energy baseline model. The result shows that electricity energy consumption patterns of highly energy demand buildings have one or two change point in correlation with the daily mean outdoor air temperature. In addition, the presented model can specify the baseline energy consumptions from the whole building electricity meter which generally aggregates all energy consumption of the buildings.