• Title/Summary/Keyword: stochastic model

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시공간구조를 가지는 확률적 강우 모형 (Multi-Site Stochastic Weather Generator for Daily Rainfall in Korea)

  • 곽민정;김용구
    • 응용통계연구
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    • 제27권3호
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    • pp.475-485
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    • 2014
  • 일반화 선형모형(GLM)에 기초한 확률적 날씨 발생기(Stochastic weather generator)는 일일 날씨를 생성하는데 가장 일반적으로 사용되는 방법인다. 본 논문에서는 다층구조를 이용하여 기존의 GLM weather generator에 공간구조를 소개하였다. 계절별 총강우량의 overdispersion 현상을 효과적으로 제거하기 위해서 smoothing된 계절별 총강우량을 모형에 포함하였고 공간구조를 소개하기 위해서 Stochastic weather generator의 모형계수에 공간구조를 가지는 다변량 정규분포를 가정하였다. 그리고 제안된 공간구조를 가지는 GLM weather generator 모형을 우리나라 76개 지역에서 39년간 측정된 일별 강우량 관측자료에 적용하였다.

Development of Dam Inflow Simulation Method Based on Bayesian Autoregressive Exogenous Stochastic Volatility (ARXSV) model

  • 파멜라 파비안;김호준;김기철;권현한
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2022년도 학술발표회
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    • pp.437-437
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    • 2022
  • The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

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스토케스틱 페트리 네트를 이용한 교통 흐름 분석 (Analysis of the traffic flow using stochastic Petri Nets)

  • 조훤;고인선
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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    • pp.1504-1507
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    • 1997
  • In this paper, we investigate a traffic flow modeled by stochastic Petri nets. The model consists of two parts : the traffic flow model and signal controller model. These models are used for analyzing the flow of the traffic intersection. The results of the evaluation are derived from a Petri Net-based simulation package, Greatspn. Through simulation we compare the performances of the pretimed signal controller with those of the trafic-adaptive signal controller.

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THE APPLICATION OF STOCHASTIC ANALYSIS TO COUNTABLE ALLELIC DIFFUSION MODEL

  • Choi, Won
    • 대한수학회보
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    • 제41권2호
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    • pp.337-345
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    • 2004
  • In allelic model X = ($\chi_1\chi$_2ㆍㆍㆍ, \chi_d$), M_f(t) = f(p(t)) - ${{\int^t}_0}\;Lf(p(t))ds$ is a P-martingale for diffusion operator L under the certain conditions. In this note, we can show existence and uniqueness of solution for stochastic differential equation and martingale problem associated with mean vector. Also, we examine that if the operator related to this martingale problem is connected with Markov processes under certain circumstance, then this operator must satisfy the maximum principle.

Asymptotic computation of Greeks under a stochastic volatility model

  • Park, Sang-Hyeon;Lee, Kiseop
    • Communications for Statistical Applications and Methods
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    • 제23권1호
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    • pp.21-32
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    • 2016
  • We study asymptotic expansion formulae for numerical computation of Greeks (i.e. sensitivity) in finance. Our approach is based on the integration-by-parts formula of the Malliavin calculus. We propose asymptotic expansion of Greeks for a stochastic volatility model using the Greeks formula of the Black-Scholes model. A singular perturbation method is applied to derive asymptotic Greeks formulae. We also provide numerical simulation of our method and compare it to the Monte Carlo finite difference approach.

A Lagrangian Stochastic Model for Turbulent Dispersion

  • Lee, Changhoon;Kim, Byunggu;Kim, Namhyun
    • Journal of Mechanical Science and Technology
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    • 제15권12호
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    • pp.1683-1690
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    • 2001
  • A Lagrangian stochastic model is adopted for the calculations of turbulent dispersion in turbulent channel flows. Dispersion of a fluid particle and relative dispersion between two particles released at the sane location are investigated and compared with the classical seating relations for homogeneous turbulence. The viscous effect is realized by adding a Browinian random walk to the calculation of the position of a particle. The near-wall accumulation of particles is examined.

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기상자료 미계측 지역의 추계학적 기상발생모형 (Stochastic Daily Weather Generations for Ungaged Stations)

  • 강문성;박승우;진영민
    • 한국농공학회지
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    • 제40권1호
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    • pp.57-67
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    • 1998
  • A stochastic weather generator which simulate daily precipitation, maximum and minimum daily temperature, relative humidity was developed. The model parameters were estimated using stochastic characteristics analysis of historical data of 71 weather stations. Spatial variations of the parameters for the country were also analyzed. Model parameters of ungauged Sites were determined from parameters of adjacent weather stations using inverse distance method. The model was verified on Suwon and Ulsan weather stations and showed good agreement between simulated and observed data.

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A Note on Estimation Under Discrete Time Observations in the Simple Stochastic Epidemic Model

  • Oh, Chang-Hyuck
    • Journal of the Korean Statistical Society
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    • 제22권1호
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    • pp.133-138
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    • 1993
  • We consider two estimators of the infection rate in the simple stochastic epidemic model. It is shown that the maximum likelihood estimator of teh infection rate under the discrete time observation does not have the moment of any positive order. Some properties of the Choi-Severo estimator, an approximation to the maximum likelihood estimator, are also investigated.

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Determination of flutter derivatives by stochastic subspace identification technique

  • Qin, Xian-Rong;Gu, Ming
    • Wind and Structures
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    • 제7권3호
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    • pp.173-186
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    • 2004
  • Flutter derivatives provide the basis of predicting the critical wind speed in flutter and buffeting analysis of long-span cable-supported bridges. In this paper, one popular stochastic system identification technique, covariance-driven Stochastic Subspace Identification(SSI in short), is firstly presented for estimation of the flutter derivatives of bridge decks from their random responses in turbulent flow. Secondly, wind tunnel tests of a streamlined thin plate model and a ${\Pi}$ type blunt bridge section model are conducted in turbulent flow and the flutter derivatives are determined by SSI. The flutter derivatives of the thin plate model identified by SSI are very comparable to those identified by the unifying least-square method and Theodorson's theoretical values. As to the ${\Pi}$ type section model, the effect of turbulence on aerodynamic damping seems to be somewhat notable, therefore perhaps the wind tunnel tests for flutter derivative estimation of those models with similar blunt sections should be conducted in turbulent flow.

A Two-stage Stochastic Programming Model for Optimal Reactive Power Dispatch with High Penetration Level of Wind Generation

  • Cui, Wei;Yan, Wei;Lee, Wei-Jen;Zhao, Xia;Ren, Zhouyang;Wang, Cong
    • Journal of Electrical Engineering and Technology
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    • 제12권1호
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    • pp.53-63
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    • 2017
  • The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.