Quantiles, which are also known as values-at-risk in finance, are often used as risk measures. Latin hypercube sampling (LHS) is a variance-reduction technique (VRT) that induces correlation among the generated samples in such a way as to increase efficiency under certain conditions; it can be thought of as an extension of stratified sampling in multiple dimensions. This paper develops asymptot...

With the rise of distributed generation, such as wind power and photovoltaic (PV), it is necessary to consider the effect of distributed generation’s output randomness. Using the method of Latin hypercube sampling (LHS) can effectively fit output scenario. Considering the sampling number of conventional LHS (CLHS) must be fixed in advance, LHS(ELHS) can be Extended to predict wind power. The sa...

McKay, Conover and Beckman (1979) introduced Latin hypercube sampling (LHS) for reducing variance of Monte Carlo simulations. More recently Owen (1992a) and Tang (1993) generalized LHS using orthogonal arrays. In the Owen's class of generalized LHS, we de ne extended Latin hypercube sampling of strengthm (henceforth denoted as ELHS(m)), such that ELHS(1) reduces to LHS. We rst derive explicit f...

Due to correlation coefficient matrix of initialized samples are not always positive definite, this paper presents the improved Latin Hypercube Sampling (LHS) methods with Evolutionary Algorithm (EA) to control correlation and handle power system probability analysis problem. To deal with the non-positive definite correlation matrix, an improved median Latin hypercube sampling with evolutionary...

Latin Hypercube Sampling (LHS) and Jittered Sampling (JS) both achieve better convergence than standard Monte Carlo Sampling (MCS) by using stratification to obtain a more uniform selection of samples, although LHS and JS use different stratification strategies. The “Koksma-Hlawka-like inequality” bounds the error in a computed mean in terms of the sample design’s discrepancy, which is a common...

abstract project management in construction industry, in many cases, is imperfect with respect to the integration of occupational health and safety (ohs) risks. this imperfection exhibits itself as complications affecting the riskiness of industrial procedures and is illustrated usually by poor awareness of ohs within project teams. difficulties on ohs regularly came about in the construction i...

Complex models can only be realized a limited number of times due to large computational requirements. Methods exist for generating input parameters for model realizations including Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. A novel extension of Latin ...

This paper investigates the suitability of Latin Hypercube sampling (LHS) for composite electric power system reliability analysis. Each sample generated in LHS is mapped into an equivalent system state and used for evaluating the annualized system and load point indices. DC loadflow based state evaluation model is solved for each sampled contingency state. The indices evaluated are loss of loa...

We develop efficient Monte Carlo methods for estimating the failure probability of a system. An example of the problem comes from an approach for probabilistic safety assessment of nuclear power plants known as riskinformed safety-margin characterization, but it also arises in other contexts, e.g., structural reliability, catastrophe modeling, and finance. We estimate the failure probability us...

This paper presents the conditioned Latin hypercube as a sampling strategy of an area with prior information represented as exhaustive ancillary data. Latin hypercube sampling (LHS) is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. It provides a full coverage of the range of each variable by maximally stratifying the mar...