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Residential buildings account for more than 20 percent of total energy usage in the U.S. in the past decade. Reduction of household energy consumption has environmental and economic impacts. As the first step, building scientists and construction engineers have endeavored efforts to obtain an accurate energy use prediction; however, few have focused on the relationship between construction cost and energy use. This paper investigates the associations between detailed construction cost takeoffs and actual energy uses in multifamily green buildings. The researchers employ advanced machine learning analytics to model the correlations between construction cost and energy use data which were collected from multifamily residential units. Findings identify the cost divisions in the construction stage that significantly correlates the energy use in the operational stage. The model allows developers to predict the energy consumption based on the construction costs, and enables them to adjust the investment strategies to amplify the energy efficiency of green building technologies.
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Mo et al. (2017) studied this question.
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