Generation of weather files using resampling techniques: an exploratory study

Simulating a building to predict its performance over the course of a full year requires an accurate repre-sentation of the stable and representative weather pat-terns of a location, i.e. a weather file. While weather file providers give due consideration to the stochastic nature of weather data, simulation is currently deter-ministic in the sense that using one weather file al-ways generates one performance outcome (for a given set of building parameters). Using a single time se-ries or aggregated number makes further analysis and decision-making simpler, but this overstates the cer-tainty of the result of a simulation. In this paper, we investigate the advantages and disadvantages of incor-porating resampling in the overall simulation work-flow by comparing commonly used weather files with synthetic files created by resampling the temperature time series from the same weather files. While pre-vious studies have quantified uncertainty in building simulation by looking at the calculation itself, this pa-per proposes a way of generating multiple synthetic weather files to obtain better estimates of expected per-formance. As case studies, we examined the perfor-mance of the ‘original’ and synthetic files for each of a sample of world climates.