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

Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption. However, most machine learning methods are primarily used for prediction through complicated learning processes at the expense of interpretability....

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Bibliographic Details
Published inBuilding simulation Vol. 14; no. 6; pp. 1737 - 1755
Main Authors Choi, Doowon, Kim, Chul
Format Journal Article
LanguageEnglish
Published Beijing Tsinghua University Press 01.12.2021
Springer Nature B.V
Subjects
Algorithms
Atmospheric Protection/Air Quality Control/Air Pollution
Building Construction and Design
Commercial buildings
Energy consumption
Engineering
Engineering Thermodynamics
Heat and Mass Transfer
Machine learning
Monitoring/Environmental Analysis
Office buildings
Partitioning
Research Article
Retrofitting
Subgroups
CBECS, commercial building
decision tree analysis
MOdel-Based recursive partitioning (MOB) algorithm
recursive partitioning
subgroup identification
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Summary:Numerous previous literature has attempted to apply machine learning techniques to analyze relationships between energy variables in energy consumption. However, most machine learning methods are primarily used for prediction through complicated learning processes at the expense of interpretability. Those methods have difficulties in evaluating the effect of energy variables on energy consumption and especially capturing their heterogeneous relationship. Therefore, 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 consumption. With resultant tree-structured subgroups, the MOB tree reveals the heterogeneous effect of energy variables and mutual influences on building energy consumptions. The results of this study would provide insights for architects and engineers to develop energy conservative design and retrofit in U.S. office buildings.
ISSN:1996-3599
1996-8744
DOI:10.1007/s12273-021-0777-8