A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Due to rapid urbanization, building energy consumption has considerably increased over the past decades. The building sector's share of global energy use and greenhouse gas (CO2) emissions is estimated to be as high as 30 percent and 40 percent, respectively. To improve the energy efficiency of building designs, the design parameters that may influence building energy use must be accurately defined and predicted. The aim of this study is to investigate the set of parameters that significantly concerned with building energy consumption. Utilizing the U.S. Commercial Buildings Energy Consumption Survey database, 22 design parameters were identified through the analysis of the literature. Then, a pre-processing, including missing value handling and outlier rejection, was performed on the collected data. Four well-known feature selection methods were examined to select more representative parameters. Then the baseline model was applied to investigate the set of parameters influencing building energy consumption. The results of this study demonstrated that the set of parameters selected by stepwise regression has the strongest influence on building energy consumption.
Due to rapid urbanization, building energy consumption has considerably increased over the past decades. The building sector's share of global energy use and greenhouse gas (CO2) emissions is estimated to be as high as 30 percent and 40 percent, respectively. To improve the energy efficiency of building designs, the design parameters that may influence building energy use must be accurately defined and predicted. The aim of this study is to investigate the set of parameters that significantly concerned with building energy consumption. Utilizing the U.S. Commercial Buildings Energy Consumption Survey database, 22 design parameters were identified through the analysis of the literature. Then, a pre-processing, including missing value handling and outlier rejection, was performed on the collected data. Four well-known feature selection methods were examined to select more representative parameters. Then the baseline model was applied to investigate the set of parameters influencing building energy consumption. The results of this study demonstrated that the set of parameters selected by stepwise regression has the strongest influence on building energy consumption.
Investigating the Set of Parameters Influencing Building Energy Consumption
International Conference on Sustainable Design and Construction (ICSDC) 2011 ; 2011 ; Kansas City, Missouri
ICSDC 2011 ; 211-221
2012-01-04
Conference paper
Electronic Resource
English
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