On the application of clustering techniques for office buildings’ energy and thermal comfort classificationOn the application of clustering techniques for office buildings’ energy and thermal comfort classification Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-10-152012enThe 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.http://creativecommons.org/licenses/by/4.0/IEEE Transactions on Smart Grid432196 - 2210 Stavrakakis Georgios Σταυρακακης Γεωργιος Kolokotsa Dionysia Κολοκοτσα Διονυσια Nikolaou Theodoros Νικολαου Θεοδωρος Skias Ioannis Σκιας Ιωαννης Institute of Electrical and Electronics Engineers Energy efficiency in buildings