Decomposing core energy factor structure of U.S. commercial buildings through clustering around latent variables with Random Forest on large-scale mixed data

Accurate selection of explanatory factors is critical for precise quantitative energy analysis (e.g. benchmarking and predicting) to support the sustainability strategy in commercial building sector. Nevertheless, the generic guiding information on factor selection lacks. This paper addresses the research gap to decompose building energy factor structure (i.e. the interaction structure among the factors which affect building energy performance) particularly at a nation level for factor selection. Specifically, an iterative approach is developed by integrating the technical strengths of Variable Clustering and Random Forest to remove collinearity, redundancy and nonrelevance. Based on a comprehensive source database extracted from a multiframe country-wide survey, the core energy factor space is revealed for 2779 commercial buildings in the U.S. In particular, 36 principal factors are identified to reliably explain building energy efficiency variations. These factors are of sufficient independence and heterogeneity which may benefit the development of parsimonious energy modeling frameworks. The robustness of the deciphered factor structure and the representativeness of the recognized critical factors are numerically validated. These acquired results can be useful for informed decision analysis and rational policy design in commercial building sector with a lighter data burden.