Statistical analysis of the wind speed at mountain site Chopok, Slovakia, using Weibull distribution

Pobocikova, Ivana; Michalková, Mária; Simon, Jan C.; Jurasova, Daniela

doi:10.1088/1757-899x/776/1/012114

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…The overall character of the surroundings could be described as partially covered. The standard direction and wind speed at Slovak Hydrometeorological Institute (SHMI) monitoring stations is 10 m above the ground surface (as it was mentioned also in [28]).…”

Section: Description Of Location and Wind Speed Datamentioning

confidence: 92%

Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia

et al. 2022

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In the present paper, the Weibull distribution is used to analyse the wind speed data of Liptovský Mikuláš-Ondrašová (49°05′52″ N, 19°35′32″ E), situated in northern Slovakia. Analysed wind speed data were collected over the 11-year period (2005-2015) and they were recorded three times a day. The results show that the seasonal values of the shape parameter k range from 1.474 to 1.607, with yearly value of 1.546 while the seasonal values of the scale parameter c range from 2.488 to 3.010 m/s, with yearly value of 2.726 m/s. We find out that according to the coefficient of determination and root mean square error, the Weibull distribution performs well in fitting the wind speed data.

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Section: Description Of Location and Wind Speed Datamentioning

confidence: 92%

Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia

et al. 2022

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“…These parameters are determined using five distinct techniques. Table 2 summarises the most significant differences and includes the formula for calculating k and c. When the shape parameter (k) is increased, it creates the appearance of a stable (constant) wind speed in an area and the shape parameter's value has no unit [19]. The MLM method produces the highest value for k, 4.0944, followed by the MOM and EM methods, which produce nearly identical values of 3.4287 and 3.4286, respectively.…”

Section: Parameters Value Comparisonmentioning

confidence: 99%

“…The two most important parameters for Weibull distribution are shape parameter (k) and scale parameter (c) [18]. According to the literature [19]- [21], k is scattered between 1.5 and 3 and c ranges between 1.1 and 1.3 times the wind speed [20]. The following procedure selects the appropriate method after determining the wind speed distribution.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Weibull Parameters With a Novel Alternative Graphical Technique for Low Wind Speeds

et al. 2022

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This paper analyses wind speed estimation for Weibull distribution using various methods. According to a previous study, the existing methods primarily target areas with moderate to high-velocity rates, and Malaysia is a tropical country with pleasant breezes all year. As a result, this research aims to devise the most efficient method of forecasting wind speeds in low-speed areas. The researcher compared existing methods such as the Moment of Method, Empirical Method, Graphical Method, Maximum Likelihood Method and the newly proposed Alternative Graphical method. The finding indicates that the novel proposed approach, the Alternative Graphical Method, is superior regarding Goodness of Fit, Kolmogorov Smirnov and Chi-Square. For Kolmogorov Smirnov, the Alternative Graphical Method is 3.4 % better than the second-best method. At the same time, the usage of Chi-Square is again at a top position, with a 61 % disparity between it and the second and third best places. However, the Alternative Graphical Method is in second place for Anderson Darling, but the forecast performance with a minimum difference of 0.3 %. These findings imply that the Alternative Graphical Method capable of making more precise predictions than current methods.

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Application of Kaniadakis κ-Statistics to Load and Impact Distributions

Bushinskaya

Timashev²

2023

Proceedings of the 6th International Conference on Construction, Architecture and Technosphere Safety

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Statistical analysis of the wind speed at mountain site Chopok, Slovakia, using Weibull distribution

Cited by 6 publications

References 23 publications

Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia

Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia

Forecasting Weibull Parameters With a Novel Alternative Graphical Technique for Low Wind Speeds

Application of Kaniadakis κ-Statistics to Load and Impact Distributions

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