Diagnosis of building energy consumption in the 2012 CBECS data using heterogeneous effect of energy variables:A recursive partitioning approach

作     者：Doowon Choi Chul Kim Doowon Choi;Chul Kim

作者机构：Department of Industrial and Systems EngineeringCollege of EngineeringTexas A&M UniversityCollege StationTX 77843USA Indoor Air Quality Research CenterKorea Institute of Civil Engineering and Building TechnologyGoyang-Si10223R.O.Korea 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2021年第14卷第6期

页      面：1737-1755页

核心收录：

学科分类：081302[工学-建筑设计及其理论] 08[工学] 0813[工学-建筑学] 

主　　题：CBECS,commercial building decision tree analysis MOdel-Based recursive partitioning(MOB)algorithm recursive partitioning subgroup identification 

摘      要：Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy ***,most machine learning methods are primarily used for prediction through complicated learning processes at the expense of *** methods have difficulties in evaluating the effect of energy variables on energy consumption and especially capturing their heterogeneous ***,to identify the energy consumption of the heterogeneous relationships in actual buildings,this study applies the MOdel-Based recursive partitioning(MOB)algorithm to the 2012 CBECS survey data,which would offer representative information about actual commercial building characteristics and energy *** resultant tree-structured subgroups,the MOB tree reveals the heterogeneous effect of energy variables and mutual influences on building energy *** results of this study would provide insights for architects and engineers to develop energy conservative design and retrofit in *** buildings.