Optimization under economic uncertainty using a net zero energy commercial office case study

Energy modeling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves uncertainties such as predicting occupant behavior, future climate, and econometric parameters. As presently practiced, energy modelers typically do not quantify the implications of these unknowns into performance outcomes. This paper describes an energy modeling approach to quantify economic risk and better inform decision makers of the economic feasibility of a project. The proposed methodology suggests how economic uncertainty can be quantified within an optimization framework. This approach improves modeling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results. The methodology is demonstrated using a net zero energy commercial office building case study located in London, ON, Canada.