New extension of Morris method for sensitivity analysis of building energy models. Menberg, K., Heo, Y., Augenbroe, G., & Choudhary, R. 09 2016.
abstract   bibtex   
Sensitivity analysis is commonly used in numerical modelling to identify those inputs that have a large impact on model outcomes. We scrutinise the Morris method, known to be computationally efficient for parameter screening, through a case study. This paper demonstrates that the current Morris method with the absolute mean as measure of parameter ranking yields unstable results. We show that using the median value, which is less sensitive to outliers, yields more robust parameter rankings for evaluations with small sample sizes. The performance of the improved Morris method is validated against the variance-based sensitivity analysis. We also investigate correlations between elementary effects and parameter values and find that they can be efficiently used to identify higher-order parameter interactions from a single set of samples used in the Morris method.
@conference{Menberg2016Newextension,
author = {Kathrin Menberg and Yeonsook Heo and Godfried Augenbroe and Ruchi Choudhary},
booktitle = {Building Simulation and Optimization},
address = {Newcastle, UK},
title = {New extension of Morris method for sensitivity analysis of building energy models},
year = {2016},
month = {09},
abstract = {Sensitivity analysis is commonly used in numerical modelling to identify those inputs that have a large impact on model outcomes. We scrutinise the Morris method, known to be computationally efficient for parameter screening, through a case study. This paper demonstrates that the current Morris method with the absolute mean as measure of parameter ranking yields unstable results. We show that using the median value, which is less sensitive to outliers, yields more robust parameter rankings for evaluations with small sample sizes. The performance of the improved Morris method is validated against the variance-based sensitivity analysis. We also investigate correlations between elementary effects and parameter values and find that they can be efficiently used to identify higher-order parameter interactions from a single set of samples used in the Morris method.},
project = {b-bem}
}

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