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Title: Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model

Abstract

Variance-based sensitivity methods can provide insights into large computational models. We present a novel application of sensitivity analysis to the Biomass Scenario Model (BSM) a large and complex system dynamics model of the developing biofuels industry in the United States. We apply a two-stage sensitivity approach consisting of an initial sensitivity screening, followed by a variance decomposition approach. Identifying key system levers and quantifying their strength is not straightforward in complex system dynamics models that have numerous feedbacks and nonlinear results. Variance-based sensitivity analysis (VBSA) offers a systematic, global approach to assessing system dynamics models because it addresses nonlinear responses and interactive effects. Especially when a large model's size makes manual exploration of the input space difficult and time-consuming, the approach can help to provide a comprehensive understanding of interactions that drive model behaviors.

Authors:
 [1];  [1]; ORCiD logo [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B)
OSTI Identifier:
1468525
Report Number(s):
NREL/JA-6A20-70809
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Cornell University Library Preprint Archive
Additional Journal Information:
Journal Name: Cornell University Library Preprint Archive
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; variance based sensitivity analysis; statistical programming; R statistical programming language; biofuel; biomass; system dynamics

Citation Formats

Inman, Daniel J, Vimmerstedt, Laura J, Bush, Brian W, Stright, Dana K, and Peterson, Steve. Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model. United States: N. p., 2018. Web.
Inman, Daniel J, Vimmerstedt, Laura J, Bush, Brian W, Stright, Dana K, & Peterson, Steve. Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model. United States.
Inman, Daniel J, Vimmerstedt, Laura J, Bush, Brian W, Stright, Dana K, and Peterson, Steve. Wed . "Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model". United States.
@article{osti_1468525,
title = {Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model},
author = {Inman, Daniel J and Vimmerstedt, Laura J and Bush, Brian W and Stright, Dana K and Peterson, Steve},
abstractNote = {Variance-based sensitivity methods can provide insights into large computational models. We present a novel application of sensitivity analysis to the Biomass Scenario Model (BSM) a large and complex system dynamics model of the developing biofuels industry in the United States. We apply a two-stage sensitivity approach consisting of an initial sensitivity screening, followed by a variance decomposition approach. Identifying key system levers and quantifying their strength is not straightforward in complex system dynamics models that have numerous feedbacks and nonlinear results. Variance-based sensitivity analysis (VBSA) offers a systematic, global approach to assessing system dynamics models because it addresses nonlinear responses and interactive effects. Especially when a large model's size makes manual exploration of the input space difficult and time-consuming, the approach can help to provide a comprehensive understanding of interactions that drive model behaviors.},
doi = {},
journal = {Cornell University Library Preprint Archive},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {3}
}