There is growing concern about the potential impact of climate change on the thermal performance of buildings. Building simulation is well-suited to predict the behaviour of buildings in the future, and to quantify the risks for prime building functions like occupant productivity, occupant health, or energy use. However, on the time scales that are involved with climate change, different factors introduce uncertainties into the predictions: apart from uncertainties in the climate conditions forecast, factors like change of use, trends in electronic equipment and lighting, as well as building refurbishment / renovation and HVAC (heating, ventilation, and air conditioning) system upgrades need to be taken into account. This article presents the application of two-dimensional Monte Carlo analysis to an EnergyPlus model of an office building to identify the key factors for uncertainty in the prediction of overheating and energy use for the time horizons of 2020, 2050 and 2080. The office has mixed-mode ventilation and indirect evaporative cooling, and is studied using the UKCIP02 climate change scenarios. The results show that regarding the uncertainty in predicted heating energy, the dominant input factors are infiltration, lighting gain and equipment gain. For cooling energy and overheating the dominant factors for 2020 and 2050 are lighting gain and equipment gain, but with climate prediction becoming the one dominant factor for 2080. These factors will be the subject of further research by means of expert panel sessions, which will be used to gain a higher resolution of critical building simulation input.

Most thermal properties of construction materials used in the analysis of building performance have been measured under laboratory conditions, using a guarded hot box or hot plate apparatus. As a consequence, these properties seldom reflect the impact of actual conditions (especially moisture content) on the values of thermal conductivity and diffusivity. Hence there is a need to develop techniques that take into account local conditions, and measure building material properties in situ. One option available is the use of a thermal probe. The thermal probe technique is based on creating a line source in a material sample and measuring the temperature rise in the sample in reaction to heat being applied. Obviously the data analysis routines used to calculate thermal conductivity and diffusivity based on the temperature rise observed are crucial to the success of the technique. This work has used transient thermal simulation of a model representing a line source in an infinite material sample to generate a set of numerical data sets to validate analysis routines in conjunction with an experimental thermal probe apparatus. Findings show that by careful application of these routines, a close agreement with simulation input values can be achieved, with errors of less than one percent. This validates the analysis routines and provides a deeper appreciation of the theoretical behaviour of a thermal probe.