Integrating robustness indicators into multi-objective optimization to find robust optimal low-energy building designs

Abstract

Uncertainties can have a large influence on building performance and cause deviations between predicted performance and performance during operation. It is therefore important to quantify this influence and identify robust designs that have potential to deliver the desired performance under uncertainties. Generally, robust building designs are identified by assessing the performance of multiple design configurations under various uncertainties. When exploring a large design space, this approach becomes computationally expensive and infeasible in practice. Therefore, we propose a simulation framework based on multi-objective optimization and sampling strategies to find robust optimal designs at low computational costs. The genetic algorithm parameters of optimization are fine tuned to further enhance the computational efficiency. Furthermore, a modified fitness function is implemented to use minimax regret robustness method in the optimization loop. The implemented simulation framework can save up to 94–99% of computational time compared to full factorial approach, while identifying the same robust designs.