scholarly journals Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4965
Author(s):  
Kun Mo Lee ◽  
Min Hyeok Lee ◽  
Jong Seok Lee ◽  
Joo Young Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Parametric Bootstrap ◽  
Random Variable ◽  
Ghg Emission ◽  
Nonparametric Bootstrap ◽  
Mcs Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

scholarly journals Uncertainty of the Electricity Emission Factor Incorporating the Uncertainty of the Fuel Emission Factors

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5697
Author(s):  
Kun Mo LEE ◽  
Min Hyeok LEE
Keyword(s):  
Power Generation ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Original Data ◽  
Emission Factors ◽  
Ghg Emission ◽  
Data Set ◽  
The Mean ◽  
The Bootstrap Method

Greenhouse gas (GHG) emission from electricity generation has been recognized as one of the most significant contributors to global warming. The GHG emission factor of electricity (hereafter, electricity emission factor) can be expressed as a function of three different (average, minimum, and maximum) fuel emission factors, monthly fuel consumption, and monthly net power generation. Choosing the average fuel emission factor over the minimum and maximum fuel emission factors is the cause of uncertainty in the electricity emission factor, and thus GHG emissions of the power generation. The uncertainties of GHG emissions are higher than those of the electricity emission factor, indicating that the uncertainty of GHG emission propagates in the GHG emission computation model. The bootstrapped data were generated by applying the bootstrap method to the original data set which consists of a 60-monthly average, and minimum and maximum electricity emission factors. The bootstrapped data were used for computing the mean, confidence interval (CI), and percentage uncertainty (U) of the electricity emission factor. The CI, mean, and U were [0.431, 0.443] kg CO2-eq/kWh, 0.437 kg CO2-eq/kwh, and 2.56%, respectively.

Exact cash-account deflator for the G2++ model

2020 ◽  
Vol 07 (01) ◽  
pp. 2050009
Author(s):  
Francesco Strati ◽  
Luca G. Trussoni
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Insurance Industry ◽  
Random Variable ◽  
Time Span ◽  
Discrete Random Variable ◽  
Variable Formula ◽  
Monte Carlo Simulation Technique ◽  
Insurance Contracts ◽  
Embedded Options

In this paper, we shall propose a Monte Carlo simulation technique applied to a G2++ model: even when the number of simulated paths is small, our technique allows to find a precise simulated deflator. In particular, we shall study the transition law of the discrete random variable :[Formula: see text] in the time span [Formula: see text] conditional on the observation at time [Formula: see text], and we apply it in a recursive way to build the different paths of the simulation. We shall apply the proposed technique to the insurance industry, and in particular to the issue of pricing insurance contracts with embedded options and guarantees.

Confidence Intervals for Weibull Fast-Fracture Parameters

2002 ◽  
Author(s):  
Mandar Chati ◽  
Curtis Johnson ◽  
Ahmet Kaya ◽  
Bjoern Schenk
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Likelihood Ratio ◽  
Failure Modes ◽  
Bootstrap Method ◽  
Parametric Bootstrap ◽  
Nonparametric Bootstrap ◽  
Weibull Parameters ◽  
Fracture Data ◽  
Fast Fracture

Practical limits on number of specimens that can be tested lead to uncertainty in the estimated Weibull parameters. This paper presents an evaluation of four techniques for estimating confidence intervals for size-scaled Weibull parameters of monolithic ceramics. The techniques include normal approximation method, likelihood ratio technique, nonparametric bootstrap, and parametric bootstrap methods. For uncensored fast-fracture data, the confidence intervals for Weibull parameters are compared to the method used in ASTM Standard C1239. A simulation fracture experiment is conducted to evaluate the statistical characteristics, in particular coverage probability, of the four methods. For fast-fracture data with multiple failure modes, the statistical assessment of the confidence interval techniques for size-scaled Weibull parameters complement the existing literature. Overall, it was observed that the likelihood ratio technique and parametric bootstrap method perform very well. These techniques can also be extended for confidence interval estimation using fast-fracture data obtained from various geometry’s of test specimens and/or loading conditions (pooled data).

Markov Chain Monte Carlo simulation of the distribution of some perpetuities

1999 ◽  
Vol 31 (01) ◽  
pp. 112-134 ◽  
Author(s):  
Jostein Paulsen ◽  
Arne Hove
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Lévy Process ◽  
Sufficient Conditions ◽  
Random Variable ◽  
Levy Process ◽  
Present Value ◽  
Strong Markov

We study the present value Z ∞ = ∫0 ∞ e-X t- dY t where (X,Y) is an integrable Lévy process. This random variable appears in various applications, and several examples are known where the distribution of Z ∞ is calculated explicitly. Here sufficient conditions for Z ∞ to exist are given, and the possibility of finding the distribution of Z ∞ by Markov chain Monte Carlo simulation is investigated in detail. Then the same ideas are applied to the present value Z - ∞ = ∫0 ∞ exp{-∫0 t R s ds}dY t where Y is an integrable Lévy process and R is an ergodic strong Markov process. Numerical examples are given in both cases to show the efficiency of the Monte Carlo methods.

scholarly journals Proactive Algorithms for Job Shop Scheduling with Probabilistic Durations

2007 ◽  
Vol 28 ◽  
pp. 183-232 ◽  
Author(s):  
J. C. Beck ◽  
N. Wilson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Random Variable ◽  
Problem Size ◽  
Deterministic Scheduling ◽  
Deterministic Problem ◽  
Deterministic Solution

Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create a theoretical framework, formally showing how Monte Carlo simulation can be combined with deterministic scheduling algorithms to solve this problem. We propose an associated deterministic scheduling problem whose solution is proved, under certain conditions, to be a lower bound for the probabilistic problem. We then propose and investigate a number of techniques for solving such problems based on combinations of Monte Carlo simulation, solutions to the associated deterministic problem, and either constraint programming or tabu search. Our empirical results demonstrate that a combination of the use of the associated deterministic problem and Monte Carlo simulation results in algorithms that scale best both in terms of problem size and uncertainty. Further experiments point to the correlation between the quality of the deterministic solution and the quality of the probabilistic solution as a major factor responsible for this success.

Exit Condition for Probabilistic Assessment Using Monte Carlo Method

2010 ◽  
Vol 10 (1) ◽  
pp. 1-9
Author(s):  
Jakub Valihrach ◽  
Petr Konečný
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optimization Design ◽  
Random Variable ◽  
Probability Of Failure ◽  
Probabilistic Assessment ◽  
Theoretical Derivation ◽  
The Monte Carlo Method ◽  
The Relationship

Exit Condition for Probabilistic Assessment Using Monte Carlo Method This paper introduces a condition used to exit a probabilistic assessment using the Monte Carlo simulation, and to evaluate it with regard to the relationship between the computed estimate of the probability of failure and the target design probability. The estimation of probability of failure is treated as a random variable, considering its variance that is dependent on the number of performed Monte Carlo simulation steps. After theoretical derivation of the decision condition, it is tested numerically with regard to its accuracy and computational efficiency. The condition is suitable for optimization design using the Monte Carlo method.

scholarly journals Appropriateness of Parametric Bootstrap System for Appraise Mean Time to Failure of a Problematical System

2020 ◽  
Vol 3 (12) ◽  
pp. 161-163
Author(s):  
Amit Marmat ◽  
Ritesh Nagar
Keyword(s):  
Complex System ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Parametric Bootstrap ◽  
Density Functions ◽  
Time To Failure ◽  
Mean Time To Failure ◽  
Monte Carlo Simulation Technique ◽  
Mean Time ◽  
Boot Strapping

This is a preview paper describes a study of an Appropriateness of Parametric Bootstrap system for appraise mean time to failure of a problematical system, where system having a different failure density functions. Mechanism for complete reliability and analysis with boot strapping can be done by Monte Carlo simulation technique for the complex network has been studied. The method is used with the bridge network. A bridge network is very useful while observation of faulty complex system and provides better accuracy. The result obtained has been compared with those obtained using and Monte Carlo simulation.

scholarly journals Potentials of Gas Emission Reduction (GHG) by the Glass Sheet Industry through Energy Conservation

2021 ◽  
Vol 226 ◽  
pp. 00047
Author(s):  
Washington Purba ◽  
Erkata Yandri ◽  
Roy Hendroko Setyobudi ◽  
Hery Susanto ◽  
Satriyo Krido Wahono ◽  
...  
Keyword(s):  
Emission Reduction ◽  
Emission Factor ◽  
Ghg Emissions ◽  
Electric Energy ◽  
Electrical Energy ◽  
Flue Gases ◽  
Ghg Emission ◽  
Gas Emission ◽  
Intergovernmental Panel ◽  
Calcination Process

Sheet Glass Industry is one industry that uses 75 % natural gas energy and 25 % electricity. Using the Intergovernmental Panel on Climate Change, IPCC-2006 emission calculation method, the average greenhouses gas (GHG) emissions obtained from the calcination process obtained 112 211 t CO2 yr–1 per plant and an average emission factor (EFkl) of 0.18 CO2 t–1 yr–1 of pull. With the technology of converting heat into electrical energy, residual combustion as flue gases has the potential to be used to produce electrical energy. Referring to the analysis and calculation; one of factories has potential to generate 0.8 MW to 3 MW electric energy. It’s efficiency of 10 % to 40 % so that it can be calculated as a component of GHG emission reductions whose value is 4.6 t CO2 yr–1 to 18.7 t CO2 yr–1 per plant. With this reduction, each of the GHG emission and emission factors per plant dropped to 93 442 t CO2 yr–1 and 0.16 CO2 t-pull–1.

scholarly journals Comparing the Thiessen’s Method against simpler alternatives using Monte Carlo Simulation

2018 ◽  
pp. 125-138
Author(s):  
Marcelo Guelfi ◽  
Carlos López-Vazquez
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Confidence Interval ◽  
Bootstrap Method ◽  
Field Measurements ◽  
Expected Value ◽  
Systematic Analysis ◽  
Sample Mean ◽  
Friedman's Test ◽  
Data Location

Estimating the expected value of a function over geographic areas is problem with a long history. In the beginning of the XX-th century the most common method was just the arithmetic mean of the field measurements ignoring data location. In 1911, Thiessen introduced a new weighting procedure measuring influence through an area and thus indirectly considering closeness between them. In another context, Quenouville created in 1949 the jackknife method which is used to estimate the bias and the standard deviation. In 1979 Efron invented the bootstrap method which, among other things, is useful to estimate the expected value and the confidence interval (CI) from a population. Although the Thiessen’s method has been used for more than 100 years, we were unable to find systematic analysis comparing its efficiency against the simple mean, or even to more recent methods like jackknife or boostrap. In this work we compared four methods to estimate de expected value.  Sample mean, Thiessen, the so called here jackknifed Thiessen and bootstrap. All of them are feasible for routine use in a network of fixed locations. The comparison was made using the Friedman’s Test after a Monte Carlo simulation. Two cases were taken for study: one analytic with three arbitrary functions and the other using experimental data from daily rain measured with a satellite. The results show that Thiessen’s method is the best estimator in almost all the cases with a 95% of confidence interval. Unlike the others, the last two considered methods supply a suitable CI, but the one obtained through jackknifed Thiessen was even more accurate, opening the door for future work.

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