This paper proposes randomly-generated synthetic timeseries incorporating climate change forecasts to quantifythe variation in energy simulation due to weather inputs,i.e., a Monte Carlo analysis for uncertainty and sensitivityquantification. The method is based on the use ofa small sample (e.g., a typical year) and can generateany numbers of years rapidly. Our work builds on previouswork that has raised the need for viable complementsto the currently-standard typical or reference yearsfor simulation, and which identified the chief componentsof weather time series. While we make no special effortsto reproduce either extreme or average temperature, thesheer number of draws ensures both are seen with eitherthe same or higher probability as recent recorded data.