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

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Abstract

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.

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  1. Department of Industrial and Systems Engineering, College of Engineering, Texas A&M University, College Station, TX, 77843, USA

    Doowon Choi

  2. Indoor Air Quality Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang-Si, 10223, Korea

    Chul Kim

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  1. Doowon Choi
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  2. Chul Kim
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Choi, D., Kim, C. Diagnosis of building energy consumption in the 2012 CBECS data using heterogeneous effect of energy variables: A recursive partitioning approach. Build. Simul. 14, 1737–1755 (2021). https://doi.org/10.1007/s12273-021-0777-8

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