Grouping techniques are frequently used in urban studies as a means for partitioning building stock heterogeneity. The choice of grouping techniques and selection indices can have a significant influence on the segmentation of building stock and associated identification of representative buildings. To date, this issue has not been systematically examined nor justified. The current paper compares different grouping and partition selection techniques using residential building data from Germany. Three grouping techniques are investigated: supervised, unsupervised and semi-supervised. The unsupervised approach is addressed through three clustering algorithms in this work: agnes, diana and partition around medoids. The semi-supervised approach consists of typology-based seed buildings coupled with a distance-based grouping, while the supervised approach relies on classification rules. The resulting partitions are assessed through multiple criteria: internal indices (CH, Dunn2, Silhouette), external index (F-measure) and impact on heating demand modelling (Heating Demand Error). Results show that the algorithms and the selection indices impact the choice of representative buildings to be modelled. Moreover, considering the F-measure, similarities between the three techniques results were observed on some of the groups. Parameters to account for when selecting a grouping technique are discussed and include the number of groups, group uniformity, and compactness/separation.

分组技术经常在城市研究中用作划分建筑存量异质性的一种方法。分组技术和选择指数的选择可能对建筑物库存的分割和代表建筑物的相关标识产生重大影响。迄今为止，尚未对该问题进行系统地研究或证明其合理性。本文使用来自德国的住宅建筑数据比较了不同的分组和分区选择技术。研究了三种分组技术：有监督，无监督和半监督。在这项工作中，通过三种聚类算法解决了无监督方法：agnes，diana和围绕medoids的分区。半监督方法包括基于类型的种子建筑物以及基于距离的分组，而监督方法则依赖分类规则。通过多个标准评估结果分区：内部指标（CH，Dunn2，Silhouette），外部指标（F量度）以及对供热需求建模的影响（供热需求误差）。结果表明，算法和选择指标会影响要建模的代表性建筑物的选择。此外，考虑到F测度，在某些组上观察到了三种技术结果之间的相似性。讨论了选择分组技术时要考虑的参数，其中包括组数，组均匀性和紧密度/分离度。外部指标（F度量）及其对供热需求建模的影响（供热需求误差）。结果表明，算法和选择指标会影响要建模的代表性建筑物的选择。此外，考虑到F测度，在某些组上观察到了三种技术结果之间的相似性。讨论了选择分组技术时要考虑的参数，其中包括组数，组均匀性和紧密度/分离度。外部指标（F度量）及其对供热需求建模的影响（供热需求误差）。结果表明，算法和选择指标会影响要建模的代表性建筑物的选择。此外，考虑到F测度，在某些组上观察到了三种技术结果之间的相似性。讨论了选择分组技术时要考虑的参数，其中包括组数，组均匀性和紧密度/分离度。