Energy models are commonly used to examine the multitude of pathways to achieve high-performance buildings. As presently practiced, a deterministic approach is used to evaluate incremental design improvements to achieve performance targets. However, significant insight can be gained by examining the implications of modelling assumptions using a probabilistic approach. This paper describes a reproducible methodology which aids modelers in identifying energy and economic uncertainties due to variabilities in solar exposure. This approach improves modelling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results.
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