Abstract
This study quantitatively analyzed the impact of resident AC (Air Conditioner) use behaviors on the cooling energy use of residential buildings. Behavior models extracted from different households were applied to a building of identical performance to compare cooling energy use according to behaviors. The behavior model used in this study is an AC on/off state prediction model using random forest algorithm; three models extracted from respective households were used. To apply the AC on/off state predicted through random forest, BCVTB was utilized for a co-simulation of a data analysis tool(R) and an energy analysis tool (EnergyPlus). The results showed that despite the identical physical and system performance of the building, the cooling energy use differed by as much as 2.5 times at set temperature 24°C. This study confirmed the possibility of integrating various prediction algorithms with energy analysis tools in future studies and quantitatively reaffirmed the need for behavior studies in the cooling energy use analysis for residential buildings.
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