Decomposing core energy factor structure of U.S. residential buildings through principal component analysis with variable clustering on high-dimensional mixed data

Abstract

Numerous energy computing frameworks were created with the aim of sustaining energy efficiency strategy to achieve residential sustainability in the U.S. While beneficial, without generic information on factor structure within building energy systems, most extant instruments are inclined to scope explanatory factor variables subjectively and diversely. Consequently, their intended utility often decreases with potential unstableness and limited generalizability among complicated energy system interactions. To overcome these issues, this paper develops a novel systematic homogeneity decomposition approach combining variable clustering and principal component analysis to identify key energy factor structure of residential buildings at the U.S. nation level. This study quantitatively results that, 32 key inter-heterogeneous energy variables (mean variance inflation factor=1.21) appear sufficient to robustly profile the U.S. residential systems with an average Pearson correlation of 0.86 while reducing data burden by 68%. Top three significant variables relate to heating degree days, indoor environment and building vintage, respectively explaining 13%, 11% and 9% of energy variations. Thus, two major contributions are expected as follows. (1) These above obtained quantitative results can provide objective information for decision makers to sensibly select critical variables for robust energy computing with improved interpretability and generalizability by commonly using the above simplified 32-factor space (extracted from a 99-factor space) while saving data cost. (2) The developed novel approach can be useful in other countries for energy factor structure decomposing purpose since it has no geographical restrictions.