2013
DOI: 10.1016/j.enbuild.2012.10.046
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Method and case study of quantitative uncertainty analysis in building energy consumption inventories

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Cited by 25 publications
(14 citation statements)
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“…by iterating 10,000 simulation runs). This technique has been applied in a number of probabilistic analysis [22][23][24][25][26] .The Latin Hypercube Sampling method is proposed to generate those random numbers, because it more precisely reflects the shape of a defined distribution with relative small sample sizes [26].…”
Section: Probabilistic Risk Assessment Of Energy Saving Shortfall Promentioning
confidence: 99%
See 1 more Smart Citation
“…by iterating 10,000 simulation runs). This technique has been applied in a number of probabilistic analysis [22][23][24][25][26] .The Latin Hypercube Sampling method is proposed to generate those random numbers, because it more precisely reflects the shape of a defined distribution with relative small sample sizes [26].…”
Section: Probabilistic Risk Assessment Of Energy Saving Shortfall Promentioning
confidence: 99%
“…Based on the results of the sensitivity analysis, the first seven parameters were considered as the influential parameters and, for them, representative PDF were developed to simulate their yearly variations during the post-retrofit period. The selection of the representative distribution followed the three established tests, namely the Chi-square test, the Kolmogorov-Smirnov (K-S) test, and the Anderson-Darling (A-D) test [21,26]. The K-S test was used when the sample sizes were less than 30.…”
Section: Probability Distribution Of the Selected Parametersmentioning
confidence: 99%
“…Instead, the probability distribution is first divided into ranges of equal probability, and then one sample is taken from each range [39]. For some applications with a given simulation sample size, LHS is a more precise numerical simulation method than MCS [39]. In the Analytica®, median Latin hypercube is preferred to random Latin hypercube due to its high accuracy that is why it is set as the default sampling method.…”
Section: Methods For Simulating Uncertainty Propagationmentioning
confidence: 99%
“…In LHS, the values of each uncertain input are not randomly generated. Instead, the probability distribution is first divided into ranges of equal probability, and then one sample is taken from each range [39]. For some applications with a given simulation sample size, LHS is a more precise numerical simulation method than MCS [39].…”
Section: Methods For Simulating Uncertainty Propagationmentioning
confidence: 99%
“…According to L PerezLombard et al, 1 R Ramanathan,2 it is shown that since 1984, primary energy has grown by 49% and CO 2 emissions by 43%, with an average annual increase of 2% and 1.8%, respectively. Current predictions, made by L Belussi and L Danza 3 and Y Lu et al, 4 show that this growth will continue. N Fumo et al, 5 R Saidur, 6 and JE Seem 7 discussed the relationship linking energy consumption with economic development and population growth and attempted to reverse this trend by increasing energy efficiency.…”
Section: Introductionmentioning
confidence: 99%