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

Seung Yeoun Choi and Sean Hay Kim ()
Additional contact information
Seung Yeoun Choi: Han-il Mechanical & Electrical Consultant, Seoul 07271, Korea
Sean Hay Kim: School of Architecture, Seoul National University of Science and Technology, Seoul 01811, Korea

Energies, 2022, vol. 15, issue 18, 1-25

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.

Keywords: energy efficient building; meta-model; feasibility analysis; decision support; conditional inference tree (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/18/6620/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/18/6620/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:18:p:6620-:d:911574

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().