scholarly journals A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 633 ◽  
Author(s):  
Ju-Young Shin ◽  
Changsam Jeong ◽  
Jun-Haeng Heo
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Statistical Method ◽  
Scaling Property

Calculation of Weibull Distribution parameters at low wind speed and performance analysis

2021 ◽  
pp. 1-24
Author(s):  
Yusuf Alper Kaplan
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Statistical Indicator ◽  
Low Wind Speed ◽  
Distribution Parameters ◽  
Wind Energy Potential ◽  
And Performance

In this study, the compatibility of the real wind energy potential to the estimated wind energy potential by Weibull Distribution Function (WDF) of a region with low average wind speed potential was examined. The main purpose of this study is to examine the performance of six different methods used to find the coefficients of the WDF and to determine the best performing method for selected region. In this study seven-year hourly wind speed data obtained from the general directorate of meteorology of this region was used. The root mean square error (RMSE) statistical indicator was used to compare the efficiency of all used methods. Another main purpose of this study is to observe the how the performance of the used methods changes over the years. The obtained results showed that the performances of the used methods showed slight changes over the years, but when evaluated in general, it was observed that all method showed acceptable performance. Based on the obtained results, when the seven-year data is evaluated in this selected region, it can be said that the MM method shows the best performance.

scholarly journals Estudio preliminar de caracterización de la velocidad del viento para instalar un aerogenerador de 400 W

2019 ◽  
pp. 23-30
Author(s):  
Gustavo Adolfo Fajardo-Pulido ◽  
Juan Carlos Juan Carlos ◽  
Gerardo Fuster-Lopez
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Turbine ◽  
Quintana Roo ◽  
Wind Rose ◽  
Scientific Advance ◽  
Method Of Calculation ◽  
Permissible Range

The characterization of wind speed in Cancun, Q. Roo Mexico, had as objectives: 1. To estimate the efficiency and energy produced by a 400W wind turbine at a height of 10 m; 2. To carry out the wind speed characterization. The methodology used was the Weibull distribution. In order to calculate the distribution of the wind speed, with the Wind Rose software we analyzed the energy in different directions and the calculation of potential wind energy based on Rayleigh's analysis. The results showed: that the power generated from the wind speed calculated in (PV) 2.8 m/s was 1.48 W, its capacity factor at 0.004 which does not reach the permissible range of 0.25 to 0.40; the energy produced annually was 14.02 kW/year, it is required to raise the wind turbine to 13.4 m, to reach 12 m/s speed and to be efficient to install a 400 W wind turbine. The paper identifies the preliminary activities and illustrates the method of calculation of wind characterization and energy produced to define the installation conditions of the wind turbine. It also contributes to the scientific advance by estimating the characterization of the wind in Cancun Quintana Roo, Mexico, for future wind turbine installations.

Wind speed distribution modeling for wind power estimation: Case of Agadir in Morocco

2018 ◽  
Vol 43 (2) ◽  
pp. 190-200 ◽  
Author(s):  
Ijjou Tizgui ◽  
Fatima El Guezar ◽  
Hassane Bouzahir ◽  
Brahim Benaid
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Goodness Of Fit ◽  
Statistical Tests ◽  
A Priori ◽  
Speed Distribution ◽  
Distribution Modeling ◽  
Wind Speed Distribution

To estimate a wind turbine output, optimize its dimensioning, and predict the economic profitability and risks of a wind energy project, wind speed distribution modeling is crucial. Many researchers use directly Weibull distribution basing on a priori acceptance. However, Weibull does not fit some wind speed regimes. The goal of this work is to model the wind speed distribution at Agadir. For that, we compare the accuracy of four distributions (Weibull, Rayleigh, Gamma, and lognormal) which have given good results in this yield. The goodness-of-fit tests are applied to select the effective distribution. The obtained results explain that Weibull distribution is fitting the histogram of observations better than the other distributions. The analysis deals with comparing the error in estimating the annual wind power density using the examined distributions. It was found that Weibull distribution presents minimum error. Thus, wind energy assessors in Agadir can use directly Weibull distribution basing on a scientific decision made via statistical tests. Moreover, assessors worldwide can use the followed methodology to model their wind speed measurements.

scholarly journals Comparison Of Three Numerical Methods For Estimating Weibull Parameters Using Weibull Distribution Model In Nigeria

2020 ◽  
Vol 27 (2) ◽  
pp. 8-15
Author(s):  
J.A. Oyewole ◽  
F.O. Aweda ◽  
D. Oni
Keyword(s):  
Standard Deviation ◽  
Wind Speed ◽  
Probability Density Function ◽  
Weibull Distribution ◽  
Probability Density ◽  
Density Function ◽  
Accurate Estimation ◽  
Weibull Parameters ◽  
Factor Method ◽  
Deviation Method

There is a crucial need in Nigeria to enhance the development of wind technology in order to boost our energy supply. Adequate knowledge about the wind speed distribution becomes very essential in the establishment of Wind Energy Conversion Systems (WECS). Weibull Probability Density Function (PDF) with two parameters is widely accepted and is commonly used for modelling, characterizing and predicting wind resource and wind power, as well as assessing optimum performance of WECS. Therefore, it is paramount to precisely estimate the scale and shape parameters for all regions or sites of interest. Here, wind data from year 2000 to 2010 for four different locations (Port Harcourt, Ikeja, Kano and Jos) were analysed and the Weibull parameters was determined. The three methods employed are Mean Standard Deviation Method (MSDM), Energy Pattern Factor Method (EPFM) and Method of Moments (MOM) for estimating Weibull parameters. The method that gave the most accurate estimation of the wind speed was MSDM method, while Energy Pattern Factor Method (EPFM) is the most reliable and consistent method for estimating probability density function of wind. Keywords: Weibull Distribution, Method of Moment, Mean Standard Deviation Method, Energy Pattern Method

A hybrid statistical method to predict wind speed and wind power

2010 ◽  
Vol 35 (8) ◽  
pp. 1857-1861 ◽  
Author(s):  
Hui Liu ◽  
Hong-Qi Tian ◽  
Chao Chen ◽  
Yan-fei Li
Keyword(s):  
Wind Speed ◽  
Statistical Method ◽  
Wind Power

scholarly journals Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

2020 ◽  
pp. 71-83
Author(s):  
Drissa Boro ◽  
Ky Thierry ◽  
Florent P. Kieno ◽  
Joseph Bathiebo
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Burkina Faso ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Likelihood Method ◽  
Distribution Model ◽  
Coefficient Of Determination ◽  
Inverse Gaussian ◽  
Study Sites

In order to estimate the power output of a wind turbine, optimise its sizing and forecast the economic rate of return and risks of a wind energy project, wind speed distribution modelling is crucial. For which, Weibull distribution is considered as one of the most acceptable model. However, this distribution does not fit certain wind speed regimes. The objective of this study is to model the frequency distribution of the three-hourly wind speed at ten sites of Burkina Faso. In this context, we compared the accuracy of five distributions (Weibull, Hybrid Weibull, Rayleigh, Gamma and inverse Gaussian) which gave satisfactory results in this field. The maximum likelihood method was used to fit the distributions to the measured data. According to the statistical analysis tools (the coefficient of determination and the root mean square error), it was found that the Weibull distribution is most suited to the Bobo, Dédougou, Ouaga and Ouahigouya sites. On the other hand, for the sites of Bogandé, Fada and Po, the hybrid Weibull distribution is the most suitable one. As to the inverse Gaussian distribution, it is the most suitable for the Boromo, Dori and Gaoua sites. In addition, the analysis focused on comparing the mean absolute error of the annual wind power density estimation using the distributions examined. The Hybrid Weibull distribution was found to have a minimal mean absolute error for most study sites.

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