Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model
Abstract
Variance-based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance-based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. Here in this paper we show that application of variance-based sensitivity analysis to the model allows us to test for non-additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- National Renewable Energy Lab. (NREL), Golden, CO (United States); Dartmouth College, Hanover, NH (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO)
- OSTI Identifier:
- 1459456
- Alternate Identifier(s):
- OSTI ID: 1460765; OSTI ID: 1464323
- Report Number(s):
- NREL/JA-6A20-73821
Journal ID: ISSN 0883-7066
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Published Article
- Journal Name:
- System Dynamics Review
- Additional Journal Information:
- Journal Name: System Dynamics Review Journal Volume: 33 Journal Issue: 3-4; Journal ID: ISSN 0883-7066
- Publisher:
- Wiley
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 09 BIOMASS FUELS; variance based sensitivity analysis; statistical program; learning curve; experience curve; learning; biofuel; biomass
Citation Formats
Jadun, Paige, Vimmerstedt, Laura J., Bush, Brian W., Inman, Daniel, and Peterson, Steve. Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model. United Kingdom: N. p., 2018.
Web. doi:10.1002/sdr.1594.
Jadun, Paige, Vimmerstedt, Laura J., Bush, Brian W., Inman, Daniel, & Peterson, Steve. Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model. United Kingdom. https://doi.org/10.1002/sdr.1594
Jadun, Paige, Vimmerstedt, Laura J., Bush, Brian W., Inman, Daniel, and Peterson, Steve. Sun .
"Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model". United Kingdom. https://doi.org/10.1002/sdr.1594.
@article{osti_1459456,
title = {Application of a variance-based sensitivity analysis method to the Biomass Scenario Learning Model},
author = {Jadun, Paige and Vimmerstedt, Laura J. and Bush, Brian W. and Inman, Daniel and Peterson, Steve},
abstractNote = {Variance-based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance-based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. Here in this paper we show that application of variance-based sensitivity analysis to the model allows us to test for non-additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model.},
doi = {10.1002/sdr.1594},
journal = {System Dynamics Review},
number = 3-4,
volume = 33,
place = {United Kingdom},
year = {Sun Jul 08 00:00:00 EDT 2018},
month = {Sun Jul 08 00:00:00 EDT 2018}
}
https://doi.org/10.1002/sdr.1594
Works referenced in this record:
Analysis of variance designs for model output
journal, March 1999
- Jansen, Michiel J. W.
- Computer Physics Communications, Vol. 117, Issue 1-2
Making best use of model evaluations to compute sensitivity indices
journal, May 2002
- Saltelli, Andrea
- Computer Physics Communications, Vol. 145, Issue 2
Improving model understanding using statistical screening
journal, December 2009
- Taylor, Timothy R. B.; Ford, David N.; Ford, Andrew
- System Dynamics Review, Vol. 26, Issue 1
Sensitivity analysis of environmental models: A systematic review with practical workflow
journal, May 2016
- Pianosi, Francesca; Beven, Keith; Freer, Jim
- Environmental Modelling & Software, Vol. 79
Importance measures in global sensitivity analysis of nonlinear models
journal, April 1996
- Homma, Toshimitsu; Saltelli, Andrea
- Reliability Engineering & System Safety, Vol. 52, Issue 1
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
journal, February 2010
- Saltelli, Andrea; Annoni, Paola; Azzini, Ivano
- Computer Physics Communications, Vol. 181, Issue 2
Quantifying the impacts of rework, schedule pressure, and ripple effect loops on project schedule performance: Quantifying Reinforcing Loop Impacts
journal, January 2016
- Jalili, Yasaman; Ford, David N.
- System Dynamics Review, Vol. 32, Issue 1
Factorial Sampling Plans for Preliminary Computational Experiments
journal, May 1991
- Morris, Max D.
- Technometrics, Vol. 33, Issue 2
Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments
journal, January 1995
- Kleijnen, Jack P. C.
- System Dynamics Review, Vol. 11, Issue 4
Determining intervention thresholds that change output behavior patterns: Determining intervention thresholds
journal, July 2016
- Walrave, Bob
- System Dynamics Review, Vol. 32, Issue 3-4
Sensitivity analysis for models with multiple behavior modes: a method based on behavior pattern measures: Sensitivity Analysis by Behavior Pattern Measures
journal, July 2016
- Hekimoğlu, Mustafa; Barlas, Yaman
- System Dynamics Review, Vol. 32, Issue 3-4
Sensitivity analysis of graphical functions: Sensitivity Analysis of Graphical Functions
journal, July 2014
- Eker, Sibel; Slinger, Jill; van Daalen, Els
- System Dynamics Review, Vol. 30, Issue 3
Estimating the approximation error when fixing unessential factors in global sensitivity analysis
journal, July 2007
- Sobol’, I. M.; Tarantola, S.; Gatelli, D.
- Reliability Engineering & System Safety, Vol. 92, Issue 7
Statistical screening of system dynamics models
journal, January 2005
- Ford, Andrew; Flynn, Hilary
- System Dynamics Review, Vol. 21, Issue 4
Sensitivity measures,anova-like Techniques and the use of bootstrap
journal, May 1997
- Archer, G. E. B.; Saltelli, A.; Sobol, I. M.
- Journal of Statistical Computation and Simulation, Vol. 58, Issue 2