Energy models are commonly used to examine the multitudeof pathways to improve building performance. As presentlypracticed, a deterministic approach is used to evaluateincremental design improvements to achieve performancetargets. However, significant insight can be gained by examiningthe implications of modeling assumptions using a probabilisticapproach. Analyzing the effect of small perturbationson the inputs of energy and economic models can improve decisionmaking and modeler confidence in building simulationresults. This paper describes a reproducible methodologywhich aids modelers in identifying energy and economicuncertainties caused by variabilities in solar exposure. Usingan optimization framework, uncertainty is quantified acrossthe entire simulation solution space. This approach improvesmodeling outcomes by factoring in the effect of variability inassumptions and improves confidence in simulation results.The methodology is demonstrated using a net zero energycommercial office building case study.