Abstract
Building design involves a large number of design parameters and performance indicators. The Monte Carlo method enables the modeler to perform thousands of building performance simulations representing a global design space. To explore such multivariate data (Factor Mapping), the parallel coordinate plot (PCP) is a popular tool, because it is easy to use in “real-time” – even for multiple decision-makers.
However, the PCP becomes unmanageable if it contains many variables, e.g. more than 10–15. Since building simulations typically involve a lot more parameters, we would like to reduce the number of variable inputs (Factor Fixing) while considering their influence towards multiple outputs. Moreover, we would like a method to highlight changes in the PCP, which would allow us to use more variables in the PCP.
However, the PCP becomes unmanageable if it contains many variables, e.g. more than 10–15. Since building simulations typically involve a lot more parameters, we would like to reduce the number of variable inputs (Factor Fixing) while considering their influence towards multiple outputs. Moreover, we would like a method to highlight changes in the PCP, which would allow us to use more variables in the PCP.
Original language | English |
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Publication date | Dec 2016 |
Number of pages | 1 |
Publication status | Published - Dec 2016 |
Event | Eighth International Conference on Sensitivity Analysis of Model Output - 117 rue du Général Ailleret, Le Tampon, Réunion Duration: 30 Nov 2016 → 3 Dec 2016 Conference number: 8 http://samo2016.univ-reunion.fr/ |
Conference
Conference | Eighth International Conference on Sensitivity Analysis of Model Output |
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Number | 8 |
Location | 117 rue du Général Ailleret |
Country/Territory | Réunion |
City | Le Tampon |
Period | 30/11/2016 → 03/12/2016 |
Internet address |
Keywords
- Sensitivity Analysis
- Uncertainty analysis