Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree

Abstract

Energy Efficient Building (EEB) design decisions that have traditionally been made in the later stages of the design process now often need to be made as early as the feasibility analysis stage. However, at this very early stage, the design frame does not yet provide sufficient details for accurate simulations to be run. In addition, even if the decision-makers consider an exhaustive list of options, the selected design may not be optimal, or carefully considered decisions may later need to be rolled back. At this stage, design exploration is much more important than evaluating the performance of alternatives, thus a more transparent and interpretable design support model is more advantageous for design decision-making. In the present study, we develop an EEB design decision-support model constructed by a transparent meta-model algorithm of simulations that provides reasonable accuracy, whereas most of the literature used opaque algorithms. The conditional inference tree (CIT) algorithm exhibits superior interpretability and reasonable classification accuracy in estimating performance, when compared to other decision trees (classification and regression tree, random forest, and conditional inference forest) and clustering (hierarchical clustering, k-means, self-organizing map, and Gaussian mixture model) algorithms.