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., a Monte Carlo analysis for uncertainty and sensitivity quantiﬁcation. 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 previous work that has raised the need for viable complements to 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.
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