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 representation of the stable and representative weather patterns 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 deterministic in the sense that using one weather file always generates one performance outcome (for a given set of building parameters). Using a single time series or aggregated number makes further analysis and decision-making simpler, but this overstates the certainty of the result of a simulation. In this paper, we investigate the advantages and disadvantages of incorporating resampling in the overall simulation workflow by comparing commonly used weather files with synthetic files created by resampling the temperature time series from the same weather files. While previous studies have quantified uncertainty in building simulation by looking at the calculation itself, this paper proposes a way of generating multiple synthetic weather files to obtain better estimates of expected performance. As case studies, we examined the performance of the ‘original’ and synthetic files for each of a sample of world climates.