STATISTICAL METHODS FOR ASSESSMENT OF LONG-TERM HYGRO-THERMAL PERFORMANCE OF BUILDINGS
Paper i proceeding, 2010
During the last few years global warming and its effects on climate have been a debated issue. The building industry plays a large part in the human activities which can be influenced by the climate change. Energy consumption and durability of buildings depend a lot on the weather conditions. Many of the developed countries will set their strategies on the probable future conditions considering the limited resources of energy.
Working with future climate scenarios in hygro-thermal simulation of buildings extends the simulation time to tens of decades. Most of the available methods for hygro-thermal assessment of buildings are suitable for short periods. In many cases they are based on hourly or daily calculations. Though it is possible to do the simulations on an hourly basis for a long period, assessing and presenting the results demands a suitable statistical method. In meteorology different methods have been used and introduced for handling the long time data series. Some of those methods can be applied in the field of building physics for assessing the future performance of the structures.
Two statistical methods, one nonparametric and one parametric, are presented here. These methods, which have been developed and used in meteorology, are capable for analyzing the long term simulation results. The nonparametric method is used to compare different data sets and different resolutions. The parametric method, which is based on decomposition of the parameter variabilities, is useful in comparing different scenarios, boundary or initial conditions. The method provides a deep and general view of the data and measures the effects of influential parameters on the data variations.