Optimization under Economic Uncertainty Using a Net-Zero Energy Commercial Office Case-Study (OR-16-035)

Energy modelling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves early assumptions of unknowns such as predicting occupant behavior, future climate and econometric assumptions. As presently practiced, energy modelers typically do not quantify the implications of these unknown into performance outcomes. This paper describes an energy modelling 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 modelling 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.