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A probabilistic model of the thermal performance of a building envelope is proposed. The model predicts the probability density function of the heat loss over a specified period of time on the basis of the design parameters of the house, climatic characteristics of the site as well as the air change rate due to mechanical or natural ventilation. Reliability of the heat loss performance of the building envelope components exposed to various climatic conditions is analyzed, and the probability of excessive heat loss is quantified. Different design options are considered. They involve a dynamic wall strategy and various types of building ventilation.

A design strategy based on integration of the building form and structure with its external environment in order to take advantage of natural forces (wind and buoyancy effects) has been evaluated in terms of risk and reliability measures. Tools for the probabilistic analysis (First-Order Reliability Method (FORM), Monte Carlo) have been presented and applied in the probabilistic modelling and sensitivity analysis of the response function of the studied building physics problem. Sensitivity analysis of the influence of basic random variables on the probability distribution of a response function is straightforward in FORM methodology. The case-based studies of probabilistic modelling of uncertainties coupled to wind speed and temperature difference through the specified building/environment system have been presented (i.e., the distribution models of the air change rate ACH and the dynamic U value characterising thermal performance of dynamic insulation). Sensitivities of the probability model of ACH to the parameters of wind speed and temperature distributions have been estimated for the consecutive values of the air change rate using FORM methodology. Reliability of ACH turned out to be most sensitive to the shape parameter of the wind speed distribution (in two-parameter Weibull model). The probabilistic risk analysis along with the effective tools for sensitivity analysis can be used to support design decisions and also to develop better models for evaluation of building performance.