Uncertainties in building design and building energy assessment

The detailed design of buildings needs to take into account various external factors, which may be subject to uncertainties. Among these factors are prevailing weather and climate; the properties of the materials used and the standard of workmanship; and the behaviour of occupants of the building. Several studies have indicated that it is the behavioural factors that are the most important among these. Methods have been developed to estimate the extent of variability in these factors and the resulting need to take this variability into account at the design stage.

Earlier work includes a paper by Gero and Dudnik (1978) presenting a methodology to solve the problem of designing heating, ventilation and air conditioning systems subjected to uncertain demands. Since then, other authors have shown an interest in the uncertainties that are present in building design. Ramallo-González (2013) classified uncertainties in energy building assessment tools in three different groups:

Buildings have long life spans: for example, in England and Wales, around 40% of the office blocks existing in 2004 were built before 1940 (30% if considered by floor area), and 38.9% of English dwellings in 2007 were built before 1944. This long life span makes buildings likely to operate with climates that might change due to global warming. De Wilde and Coley (2012) showed how important is to design buildings that take into consideration climate change and that are able to perform well in future weathers.

The use of synthetic weather data files may introduce further uncertainty. Wang et al. (2005) showed the impact that uncertainties in weather data (among others) may cause in energy demand calculations. The deviation in calculated energy use due to variability in the weather data were found to be different in different locations from a range of (-0.5% to 3%) in San Francisco to a range of (-4% to 6%) in Washington D.C. The ranges were calculated using a Typical Meteorological Year (TMY) as the reference.

The spatial resolution of weather data files was the concern covered by Eames et al. (2011). Eames showed how a low spatial resolution of weather data files can be the cause of disparities of up to 40% in the heating demand. The reason is that this uncertainty is not understood as an aleatory parameter but as an epistemic uncertainty that can be solved with the appropriate improvement of the data resources or with specific weather data acquisition for each project.

...
Wikipedia