Uncertainty quantification for combined building performance and cost-benefit analyses

Building performance simulation is most often used to improve the design and at times the operation of buildings. Within a building model, the thermal characteristics of the envelope and the HVAC (heating, ventilation, and air conditioning) equipment are described by parameters that often cannot be estimated with high accuracy (e.g., occupant behavior, building envelope and HVAC equipment performance). Another common part of the design process of a building is a cost-benefit analysis to compare design options and different scenarios. The results are also heavily dependent on assumptions about uncertain economic parameters (e.g., future inflation rates and energy costs). In this paper a Monte Carlo based methodology for uncertainty quantification that combines the building simulation and the cost-benefit calculation is developed and demonstrated. Furthermore, Monte Carlo filtering is applied to determine the model inputs (e.g., design specifications and boundary conditions) that lead to the desired model output (e.g., a positive net present value of the investment). The aim is to propose a methodology that helps to enhance the design process or building operation and supports related decision-making.