Application of Four Probability Distributions for Wind Speed Modeling

Pobocikova, Ivana; Michalková, Mária

doi:10.1016/j.proeng.2017.06.123

Cited by 71 publications

(38 citation statements)

References 13 publications

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“…These statistical distributions are lognormal, gamma, Weibull, Pearson type V and Pearson Type VI distributions. The maximum likelihood estimation (MLE) is used to estimate the parameter values of these distributions [4,6,8,9]. The probability density function of these distributions are as follow:…”

Section: Statistical Distributionsmentioning

confidence: 99%

The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

Pongsakchat

Kidpholjaroen

2020

AJAS

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The fine particulate matter (PM2.5) concentrations is one of the most important issues that are often discussed since it has a greater impact on human health. Statistical distribution modeling plays an important role in predicting PM2.5 concentrations. This research aims to find the optimum statistical distribution model of PM2.5 in Rayong Province and Chonburi Province. The daily average data from 2014 – 2019 for Rayong and from 2015 – 2019 for Chonburi were using. Five statistical distributions were compared. A proper statistical distribution that represents PM2.5 concentrations has been chosen based on three criteria include Anderson-Darling statistic and RMSE. The results show that Pearson type VI distribution performs better compared to other distributions for PM2.5 concentrations in Rayong. For Chonburi, the proper statistical distribution is Log normal distribution.

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Section: Statistical Distributionsmentioning

confidence: 99%

The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

Pongsakchat

Kidpholjaroen

2020

AJAS

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“…Pobo?łkov, et al [21] shown that 3-parameter Weibull performs as the best for modeling the wind speed at the airport in Dolny Hri?ov. Seguro and Lambert [22] considered three methods for estimating the parameters of the Weibull wind speed distribution for wind energy analysis and they recommend the maximum likelihood method for time series wind data and the modified maximum likelihood method for wind data with frequency distribution format.…”

Section: Probability Modeling Of Monthly Maximum Sustained Wind Speedmentioning

confidence: 99%

Probability Modeling of Monthly Maximum Sustained Wind Speed in Bangladesh

Hossain

2019

Stat., optim. inf. comput.

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Wind speed is one the most important parameter of wind energy. However, the probability density functions (pdfs) are usually used to describe the characteristics of wind speed. In literature, several pdfs have been investigated to justify the suitability of modeling the wind speed in different regions all over the world. Therefore, the choice of the pdf is very crucial. This paper, firstly find the estimates of the parameters of all probability distribution considered in this study to describe wind speed characteristics by using the maximum likelihood method and iterations were carried out with Newton-Raphson technique. Finally, the appropriate pdf for monthly maximum sustained wind speed at Coxs Bazar in Bangladesh is selected with the help of the KolmogorovCSmirnov statistic, the coefficient of determination (R 2), the Chi-square (χ 2) statistic, Root mean square error (RMSE), AIC and BIC. Here, results depict that, among the distributions considered in this study, the Skewed t (ST) distribution provides the best fit to the wind speed data.

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“…The speed and source of extreme wind energy are analyzed by the estimation method [10]. Furthermore, wind velocity modeling has been done with the application of four distribution probabilities [11]. Another work explored the distributionally robust method to estimate exceedance probabiliities [12].…”

Section: Introductionmentioning

confidence: 99%

Component Analysis of Purification System of RSG-GAS

Susmikanti

Hartini

Saepudin³

et al. 2018

Jurnal Pengembangan Energi Nuklir

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COMPONENT ANALYSIS OF PURIFICATION SYSTEM OF RSG-GAS. Component reliability analysis is required in the aging management of RSG-GAS that has reached an age of 30 years. One of the required analyses is the assessment of the distribution of repair data and the estimation of related parameters. The Primary Purification System (KBE01) and the Purification and Warm Water Layer System (KBE02) are important components of RSG-GAS. By knowing the repair data distribution, the parameters of the most frequently occurring component repair and the average of the repair period can be estimated, so that the required provision of spare parts for the smooth operation of the reactor can be predicted. The purpose of this study is to analyze the components of the KBE01 and KBE02 systems through the data distribution approach using the matching test method. With the matching test, the form of data distribution can be determined, so the parameter of the average component repair period that can be used as a comparison of the maintenance period of the components can be estimated. The repair times of KBE01 and KBE02 in RSG-GAS on Core 52 through Core 88 (2006-2015) were analyzed using goodness-of-fit test. The repair times of AA068 and AP001 KBE01 follow the exponential distribution with average repair times of 631.6 and 451.2 days, respectively. The repair times of WWL and AA002 KBE02 followed an exponential distribution with average repair times of 239.5 days and 888.0 days.

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Application of Four Probability Distributions for Wind Speed Modeling

Cited by 71 publications

References 13 publications

The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

Probability Modeling of Monthly Maximum Sustained Wind Speed in Bangladesh

Component Analysis of Purification System of RSG-GAS

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