Incorporating Climate Change Predictions in the Analysis of Weather-based Uncertainty
This paper proposes randomly-generated synthetic time series incorporating climate change forecasts to quantify the variation in energy simulation due to weather inputs, i.e., Monte Carlo analysis for uncertainty and sensitivity quantification. The method is based on the use of a small sample (e.g., a typical year) and can generate any numbers of years rapidly. Our work builds on previous work that has raised the need for viable complements to the currently-standard typical or reference years for simulation, and which identified the chief components of weather time series. While we make no special efforts to reproduce either extreme or average temperature, the sheer number of draws ensures both are seen with either the same or higher probability as recent recorded data.