A new method for estimating maximum wind gust speed with a given return period and a high areal resolution

Pop, L.; Sokol, Zbyněk; Hanslian, David

doi:10.1016/j.jweia.2016.09.005

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…In order to determine the vertical level at which the WRF output better models the measured data, the atmospheric stability is considered (Pop et al, 2016). The classification of the stability states is based on the Richardson number, which compares the buoyancy and inertial forces, as shown in equation (5) (Stull, 1988)…”

Section: Resultsmentioning

confidence: 99%

“…The extreme value theory is used to model the behavior of the hourly mean monthly maximum wind speeds. Several applications use the block maxima method (Patlakas et al, 2016; Pop et al, 2016). In this case, the time series are divided into blocks of equal size and the maximum value of each block is used to create the dataset.…”

Section: Generalized Extreme Value Distributionmentioning

confidence: 99%

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An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

2019

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The aim of this study was to generate hourly mean monthly maximum wind speed return period curves for heights 60 m above the ground using Weather Research and Forecasting modeled data. The methodology introduced produces long-term wind speed data in places where the available measured series for such heights are not long enough for a correct extreme value analysis. Climate Forecast System Reanalysis data are used as input for the Weather Research and Forecasting simulations, providing information for the period 1979–2015. The modeled results are compared with wind speed series measured in anemometric towers, available for the period 2008–2015. Weather Research and Forecasting output is then adjusted to model properly the wind speed at the measuring sites. A good representation of the cumulative distribution of monthly maxima was reached after applying a double linear adjustment.

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Section: Resultsmentioning

confidence: 99%

Section: Generalized Extreme Value Distributionmentioning

confidence: 99%

An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

2019

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“…Wind conditions in a given country were, and still are one of the main issues raised by researchers in wind engineering. Recently, such works have been carried out in various countries - [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. These works use different time periods of data, different approaches to the description of parent and extreme data distributions, and different methods for estimating parameters of these distributions.…”

Section: Current Regulationsmentioning

confidence: 99%

Characteristic velocity of strong wind for wind engineering purposes

2023

Archives of Civil Engineering

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Eurocode standard recommends using fundamental basic wind velocity (characteristic velocity) as the design value in civil engineering. There are different approaches to estimate this value depending on the climate features of the given area and the quality of environmental data. The estimation of the characteristic value requires statistical analysis of historical data regarding wind velocities measured throughout the country at meteorological stations. The results of the analysis are probability density distributions of this random variable for each meteorological station. On this basis, values of characteristic wind velocity with a mean return period of 50 years are determined. The zones with uniform velocities are delineated on the map of the country. In the case of Poland the last evaluation of wind zones took place over 15 years ago. Higher quality of measurement data on the one hand, and the introduction of the second generation of Eurocode standards on the other hand, create a need to check and update these zones. This work presents theoretical basis for the estimation of characteristic values of random variables in the context of wind velocity, comprehensively reviews practical methods used for this purpose and summarizes current situation in Poland, finally discusses the issues related to the heterogeneity of wind data, illustrating them with an example.

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“…When it is looked at global radiation values for Mut region, the highest value was in June and the lowest value was in December [5]. There are many studies in the literature on statistical and data analysis of the amount of electrical energy obtained from renewable energy sources [6][7][8][9][10][11][12][13][14]. In a study, Ozay C. and Celiktaş S.M.…”

Section: Introductionmentioning

confidence: 99%

“…GWC consists of wilbull distribution of wind speed observed eight wind sectors and parameter values for wind speeds of these sectors. By using this method, a map of maximum wind power speed was calculated with 100 m. horizontal resolutiıon for the Czech Republic and its error was also calculated [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Applicability of Mersin Region Wind Speed Data with Artificial Neural Networks

Akkaya

Gültekin

Sabancı

et al. 2020

Nevşehir Bilim Ve Teknoloji Dergisi

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In this study, wind speed data were analyzed in order to provide energy to the heating and electrical systems of a house with renewable energy sources in Mersin-Mut region. Three-year wind speed data is taken from the Turkey General Directorate of Meteorology in the region. Annual estimation was made with artificial neural networks using 28-day wind speed data for the studied area. Some of the wind data were used for training of the neural network, and some were used for testing. In the artificial neural network model, the most successful model was obtained by changing the number of neurons in the hidden layer. In the analysis made using eight neurons in the hidden layer, the lowest MAE and RMSE error values were calculated.While the number of neurons was eight, MAE and RMSE values were obtained as 0.4056 and 0.5403, respectively. In addition, analysis of wind data with WAsP software has been carried out for this region. Thus, the average instantaneous wind speed was determined according to the analysis studies.

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A new method for estimating maximum wind gust speed with a given return period and a high areal resolution

Cited by 9 publications

References 21 publications

An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

An approach to model extreme wind speed distributions using the Weather Research and Forecasting model

Characteristic velocity of strong wind for wind engineering purposes

Analysis and Applicability of Mersin Region Wind Speed Data with Artificial Neural Networks

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