Towards a multidimensional segmentation of the real estate market of apartments in Santiago de Chile: a probabilistic approach from the energy building simulation

It is clear that the thermophysical properties of materials, occupancy patterns and internal gains represent some of the most important sources of uncertainty in the field of building simulation. Uncertainty and sensibility analysis deals with this situation, since it can generate a great range of forecast values based on the distribution of the input variables. However, most of the building energy simulation programs are deterministic, rather than probabilistic and consequently their results frequently are not expressed in terms of probabilities. On the contrary, the probabilistic approach requires a more complex process, since parameters quantification requires not only an assessment of the point estimate, but also an assessment of the uncertainty. Probably this situation appears as particularly critical when the focus is place on a group of cases instead of a single building case. This research aims to define the uncertainty of the predicted energy performance of apartments from the real estate market of Santiago de Chile by means of the Monte Carlo Analysis (MCA) method. These units were previously classified using the k-means clustering method with the aim of generating representative market segments. A total of 7 input parameters constitute the basis for these analyses using the standard EN ISO 13790 as calculation algorithm for estimating the annual heating demand.