Energy audit of schools by means of cluster analysis

• The potential of clustering in selecting few representative buildings is discussed.
• The regression analysis has been used to identify the most suitable parameters to cluster the sample.
• Clustering and regression analysis have been implemented in combination.
• The correlation between characteristics and real energy consumptions is improved inside each cluster.
• The sample of about 60 Italian schools has been reduced in four representative buildings.

More than 30 % of the Italian schools have very low energy efficiency due to aging or poor quality of construction. The current European policy on energy saving, with the Commission Delegated Regulation (EU) 244/2012, recommends a cost-optimal analysis of retrofit improvements, starting from some reference buildings. One relevant issue is the definition of a set of reference buildings effectively representative of the considered stock. A possible solution could be found using data mining techniques, such as the K-means clustering method, which allows the division of a large sample into more homogeneous and small groups. This work adopts the cluster analysis to find out a few school buildings representative of a sample of about 60 schools in the province of Treviso, North-East of Italy, thus reducing the number of buildings to be analyzed in detail to optimize the energy retrofit measures. Real consumption data of the scholastic year 2011–2012 were correlated to buildings characteristics through regression and the parameters with the highest correlation with energy consumption levels used in cluster analysis to group schools. This method has supported the definition of representative architectural types and the identification of a small number of parameters determinant to assess the energy consumption for air heating and hot water production.

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