M. Lurdes Simões

Application of clustering technique for definition of generic objects in a material database

In this article, generic objects are introduced into material databases of building simulation tools. A generic object has the common characteristics of one type of specific object and can represent specific ones in the simulation. The application of generic objects only requires some general design information; thus, it is convenient for the simulation users who do not have a detailed knowledge of building specification in the early design stage. First, the method to uncover the underlying cluster structure in the dataset is illustrated. Clustering of the objects depends on the adopted clustering variables and clustering procedure. Therefore, different clustering techniques are applied to agglomerate specific materials into different clusters, and the clustering solutions are compared for validation. Generic synthesis is then conducted to derive the generic material from each identified cluster. The approach on how to apply generic materials to fill in missing values of the incomplete material data is also described.

Energy and Water Consumption Variability in School Buildings: Review and Application of Clustering Techniques

AbstractIn developed countries, the building sector is responsible for a very significant share of the total energy consumption. School buildings, because they are places where children are educated and learn to become active members of the society, should be a good example of an efficient use of energy and water. In this study, data of the energy and water consumption of 23 Portuguese schools and their main building characteristics and properties were gathered. This information was normalized to homogenize the data set and then analyzed using advanced clustering techniques. The results show a significant variability in the consumption of different schools, even with similar characteristics, suggesting that the user behavior plays an important role in their efficiency. The complete linkage and Ward’s clustering methods were applied, both produced three clusters, and reference values for electricity and water consumption were defined.