Survey and Evaluate Uncertainty Quantification Methodologies

Lin, Guang; Engel, David W.; Eslinger, Paul W.

doi:10.2172/1035732

Cited by 25 publications

(22 citation statements)

References 34 publications

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“…This section shows how uncertainties are propagated through the modelling process (data-model-refinement-validation) and in the resulting estimates of annual energy consumption. The techniques for propagating uncertainties can generally be classified [13] as intrusive or non-intrusive. Intrusive methods require reformulating the mathematical physical model.…”

Section: A Taxonomy Of Key Uncertainties Using High-level Frameworkmentioning

confidence: 99%

“…The meaning of each of these terms is as follows [12]: 'Uncertainty characterization is any proposition (declaration) that measures, quantitatively or qualitatively, the degree of uncertainty associated with a parameter and prediction; Uncertainty quantification is a subset of the uncertainty characterization in which only quantitative measures (in this research probabilistic density functions) are defined for uncertain parameters and predictions; Uncertainty propagation means making inferences about the uncertainty characterization in the output predicted parameters (model results) based on the uncertainty characterization of the input parameters'. The techniques for propagating uncertainties can generally be classified [13] as intrusive or non-intrusive. Intrusive methods require reformulating the mathematical physical model.…”

Section: Introductionmentioning

confidence: 99%

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Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas

2017

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Domestic energy modelling is complex, in terms of user input and the approach used to define the model; therefore, there is an increase in the sources of uncertainties. Previous efforts to perform sensitivity and uncertainty analyses have focused on national energy models, while in this research, the objective is to extend traditional sensitivity analysis and use a local framework combining principal component regression and Monte Carlo Simulation. Therefore, in our method the total amount of the energy output's variance is decomposed, in relative terms, according to the contribution of the different predictor parameters. Our framework provides compelling evidence that local area characteristics are important in energy modelling and those national and regional indexes and values may not properly reflect the local conditions, resulting in programmes and interventions that will be sub-optimal. Furthermore, our uncertainty methodology uses a three dimensional integrative taxonomy and a concept map. The concept map identified concrete terminal causes of uncertainty within the taxonomic framework of sources, issues, sub-issues and a further abstraction of those quantities in terms of accuracy and precision. Understanding uncertainties in this way provides a possible framework for modellers, policy makers and data collectors to improve practice in key areas and to reduce uncertainty.

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Section: A Taxonomy Of Key Uncertainties Using High-level Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas

2017

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“…Aleatory uncertainty is related to the intrinsic variation of the system caused by model input parameters, which can lead to an unpredictable variation in the outcomes (Roy and Oberkampf, 2010; Lin et al, 2012). It can also be referred to as “variability,” “irreducible,” “stochastic,” or “random uncertainty” and is usually characterized using probabilistic approaches due to its random nature.…”

Section: Introductionmentioning

confidence: 99%

“…Theoretically, it can be overcome by defining a better physical-mathematical model or considering more data by carrying out thorough measurements. It is also known as “subjective” or “reducible uncertainty.” Epistemic uncertainty is not well characterized by probabilistic approaches, as it relates to the lack of knowledge, rather than statistical information (Lin et al, 2012). …”

Section: Introductionmentioning

confidence: 99%

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Mangado

Piella

Noailly

et al. 2016

Front. Bioeng. Biotechnol.

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Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

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“…Uncertainty quantification (UQ) [1] is the science of quantitative characterization and reduction of uncertainties in applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.…”

Section: Introductionmentioning

confidence: 99%

Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow

Sun

Mao

et al. 2016

Energies

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An intrusive spectral method of probabilistic load flow (PLF) is proposed in the paper, which can handle the uncertainties arising from renewable energy integration. Generalized polynomial chaos (gPC) expansions of dependent random variables are utilized to build a spectral stochastic representation of PLF model. Instead of solving the coupled PLF model with a traditional, cumbersome method, a modified stochastic Galerkin (SG) method is proposed based on the P-Q decoupling properties of load flow in power system. By introducing two pre-calculated constant sparse Jacobian matrices, the computational burden of the SG method is significantly reduced. Two cases, IEEE 14-bus and IEEE 118-bus systems, are used to verify the computation speed and efficiency of the proposed method.

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Survey and Evaluate Uncertainty Quantification Methodologies

Abstract: This document was printed on recycled paper.

Cited by 25 publications

References 34 publications

Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas

Using a Local Framework Combining Principal Component Regression and Monte Carlo Simulation for Uncertainty and Sensitivity Analysis of a Domestic Energy Model in Sub-City Areas

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow

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