Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization

Building performance simulation (BPS) uses computer-based models that cover performance aspects such as energy consumption and thermal comfort in buildings. The uptake of BPS in current building design projects is limited. Although there is a large number of building simulation tools available, the actual application of these tools is mostly restricted to code compliance checking or thermal load calculations for sizing of heating, ventilation and air-conditions systems in detailed design. The aim of the presented work is to investigate opportunities in BPS during the later phases of the design process, and to research and enable innovative applications of BPS for design support. The research started from an existing and proven design stage specific simulation software tool. The research methods applied comprise of literature review, interviews, rapid iterative prototyping, and usability testing. The result of this research is a prototype simulation based environment that provides add-ons like uncertainty and sensitivity analysis, multi-criteria and disciplinary decision making under uncertainty, and multi-objective optimization. The first prototype addressing the uncertainties in physical, scenario, and design parameters provides additional information through figures and tables. This outcome helps the designer in understanding how parameters relate to each other and to comprehend how variations in the model input affect the output. It supports the design process by providing a basis to compare different design options and leads therefore to an improved guidance in the design process. The second approach addresses the integration of a decision making protocol with the extension of uncertainty and sensitivity analysis. This prototype supports the design team in the design process by providing a base for communication. Furthermore, it supports the decision process by providing the possibility to compare different design options by minimizing the risk that is related to different concepts. It reduces the influence of preoccupation in common decision making and avoids pitfalls due to alack of planning and focus. The third and last approach shows the implementation of two multi-objective algorithms and the integration of uncertainty in optimization. The results show the optimization of parameters for the objectives energy consumption and weighted over and underheating hours. It shows further how uncertainties impact the Pareto frontier achieved. The applicability and necessity of the three implemented approaches has further been validated with the help of usability testing by conducting mock-up presentations and an online survey. The outcome has shown that the presented results enhance the capabilities of BPS and fulfil the requirements in detailed design by providing a better understanding of results, guidance through the design process, and supporting the decision process. All three approaches have been found important to be integrated in BPS