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A generalizable framework for urban building energy model archetype generation using k-prototypes mixed-data clustering
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A generalizable framework for urban building energy model archetype generation using k-prototypes mixed-data clustering
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Journal Article
A generalizable framework for urban building energy model archetype generation using k-prototypes mixed-data clustering
2025
Overview
Developing building archetypes from urban data is essential for urban building energy modeling (UBEM), yet current approaches lack generalizability, particularly regarding categorical data. This paper compares k-means, k-medoids, and k-prototypes clustering using Zurich’s building registry to evaluate the impact of preprocessing and algorithm choice. Results show preprocessing had limited effect on clustering, while k-prototypes—capable of handling numerical and categorical data concurrently—produced practical, interpretable clusters. The method’s simplicity and robust handling of mixed-type data suggest k-prototypes can streamline UBEM archetype generation workflows.
Publisher
IOP Publishing
Subject
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