Exploring energy certificates of buildings through unsupervised data mining techniques

Energy Certificates of Buildings (ECB) provide interesting information on the standard energy performance, thermo-physical and geometrical related properties of existing buildings. The analysis of such data collection is challenging due to data volume and heterogeneity of attributes. This paper presents EPICA a data mining framework to automatically explore a collection of ECB to extract interesting knowledge items. To this aim, EPICA first reduces the data dimensionality through the Principal Component Analysis, then a clustering algorithm is exploited to discover groups of ECB with similar features. Each group is then locally characterized by a set of relevant generalized association rules able to summarize interesting relations among variables influencing energy performance of buildings at different coarse granularities. Experimental results, obtained on real data collected from an energy certification dataset related to Piedmont Region, in North Western of Italy, shows the effectiveness of EPICA in extracting a manageable set of human-readable knowledge items characterizing the groups of buildings with different energy performance levels