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

R.R. Kotireddy (Corresponding author), P. Hoes, J.L.M. Hensen

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
87 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)546-565
Number of pages20
JournalJournal of Building Performance Simulation
Volume12
Issue number5
Early online date3 Oct 2018
DOIs
Publication statusPublished - 3 Sep 2019

Keywords

  • low-energy buildings
  • Multi-objective optimization
  • performance robustness assessment
  • robust design
  • scenario sampling
  • uncertainties

Fingerprint Dive into the research topics of 'Integrating robustness indicators into multi-objective optimization to find robust optimal low-energy building designs'. Together they form a unique fingerprint.

Cite this