Towards robust low-energy houses. A computational approach for performance robustness assessment using scenario analysis
CHALLENGE - The aim of this research is to develop a computational performance robustness assessment (CPRA) approach to assess the performance robustness of low-energy houses considering future scenarios to identify robust designs for various decision makers that have the potential to deliver the desired performance over building's life-span.
SOLUTION - The developed CPRA approach is implemented using a simulation framework, and to make the simulation framework more efficient and relevant in practice, scenario sampling using the uniform Latin hypercube sampling method and a NSGA-II genetic algorithm based multi-objective optimization method are implemented. modeFRONTIER has been used in pre-processing for scenario sampling strategies and in post-processing for Multi-Criteria Decision Making analysis.
BENEFITS - While identifying the same robust designs, the implemented simulation framework can save up to 94-99.9% of computational time compared to a full factorial approach, which require millions of simulations. Using the developed CPRA approach, a decision maker can select a robust design from the large design space based on optimal performance and performance robustness or can trade off the selected robust design s with required additional investment cost.