Probabilistic Modelling of Air Infiltration and Heat Loss in Low-Rise Buildings
A probabilistic model (PROMO) of air exchange and heat loss in a low-rise building with a lightweight construction is proposed. The probability distributions of the air change rate and of the heat loss are the main outputs of the model. These distributions are derived from the input probability distributions of climatic data and the assumed properties of the building, using deterministic models of the physical processes involved in air exchange and heat transfer. The output distributions are approximated using the First Order Reliability Method (FORM). They are further used to perform a risk analysis of air exchange and heat loss in a building.
The input data to the model are properties of the statistical distributions of the wind velocity, wind direction and external temperature at a suitable meteorological station. Further, relevant topographic details at the location of the building and the positioning of the building with respect to the geographic directions are needed. Also, data concerning the building, such as its geometry, leakage properties and thermal transmittance are required. The leakage properties and thermal transmittance are assumed to be climate dependent in the model. Finally, the serviceability limits for the risk analysis should be specified.
The infiltration part of the PROMO model was validated on a particular house situated in the vicinity of Göteborg. The ACH distributions calculated from the results of the full-scale pressure measurements conformed to the predictions from the model.
Probability density functions of ACH were estimated for selected wind directions for a building situated in two different temperature zones. The results quantify the influence of wind and temperature difference. When only stack effect is considered (e.g. when the building is sheltered from wind) the ACH distribution is usually close to normal. For combined stack and wind effects the best fit is obtained with a log-normal distribution. The distributions of heat losses are generally close to Weibull.
Risk analysis may contribute to the development of tools for design of energy efficient, healthy buildings.