On the Application of Clustering Techniques for Office Buildings' Energy and Thermal Comfort Classification

The aim of this paper is to develop and propose an integrated classification method for the determination of office buildings' energy and thermal comfort rating classes. The applications of five clustering techniques: Hierarchical, K-Means, Gaussian Mixture Models, Fuzzy, and Neural algorithms to a large building dataset are tested in order to investigate the appropriate method for establishing energy and thermal comfort classifications. For the clustering results testing, three internal validity indices: the Silhouette, the Davies Bouldin, and the Dunn Index have been applied, in order to select the appropriate number of clusters and the most efficient algorithm for each case. The proposed classification approach is also evaluated through comparisons with the methodologies that are recommended by the European standards. The classification results are used for a parametric study of common buildings' characteristics in each rating class, in order to provide with a tool for adopting improvement recommendations for buildings' energy efficiency.