Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates

As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.