scholarly journals Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

2015 ◽  
Vol 159 (2) ◽  
pp. 329-348 ◽  
Author(s):  
Sven-Erik Gryning ◽  
Rogier Floors ◽  
Alfredo Peña ◽  
Ekaterina Batchvarova ◽  
Burghard Brümmer
Keyword(s):  
Wind Speed ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Distribution Parameters ◽  
Wind Lidar

Application of differential evolution for wind speed distribution parameters estimation

2021 ◽  
pp. 0309524X2199996
Author(s):  
Rajesh Kumar ◽  
Arun Kumar
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Speed Distribution ◽  
De Algorithm ◽  
Wind Speed Distribution ◽  
Distribution Parameters

Weibull distribution is an extensively used statistical distribution for analyzing wind speed and determining energy potential studies. Estimation of the wind speed distribution parameter is essential as it significantly affects the success of Weibull distribution application to wind energy. Various estimation methods viz. graphical method, moment method (MM), maximum likelihood method (ML), modified maximum likelihood method, and energy pattern factor method or power density method have been presented in various reported research studies for accurate estimation of distribution parameters. ML is the most preferred approach to study the parameter estimation. ML works on the principle of forming a likelihood function and maximizing the function for parameter estimation. ML generally uses the numerical based iterative method, such as Newton–Raphson. However, the iterative methods proposed in the literature are generally computationally intensive. In this paper, an efficient technique utilizing differential evolution (DE) algorithm to enhance the estimation accuracy of maximum likelihood estimation has been presented. The [Formula: see text] of GA-Weibull, SA-Weibull, and DE-Weibull is 0.958, 0.953, and 0.973 respectively, and value of RMSE of DE-Weibull 0.0083, GA-Weibull (0.0104), and SA-Weibull (0.0110), for the yearly wind speed data are obtained. The lowest root mean square error and larger regression value for both monthly and yearly wind speed data indicate that the DE-Weibull distribution has the best goodness of fit and advocate the DE algorithm for the parameter estimation.

Comparison of Three Methods to Estimate Wind Speed Distribution

2011 ◽  
Author(s):  
D. K. Kirova ◽  
Michail D. Todorov ◽  
Christo I. Christov
Keyword(s):  
Wind Speed ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals Artificial Intelligence Techniques for Estimation of Optimum Weibull Parameters for Wind Speed Distribution

2019 ◽  
Vol 8 (4S2) ◽  
pp. 150-156
Keyword(s):  
Wind Speed ◽  
Tamil Nadu ◽  
Optimization Algorithms ◽  
Coefficient Of Determination ◽  
Short Term ◽  
Weibull Parameters ◽  
Wind Speed Data ◽  
Speed Distribution ◽  
Wind Speed Distribution

The main objective of this study is to estimate the optimum Weibull scale and shape parameters for wind speed distribution at three stations of the state of Tamil Nadu, India using Nelder-Mead, Broyden–Fletcher–Goldfarb–Shanno, and Simulated annealing optimization algorithms. An attempt has been made for the first time to apply these optimization algorithms to determine the optimum parameters. The study was conducted for long term wind speed data (38 years), short term wind speed data (5 years) and also with single year’s wind speed data to assess the performance of the algorithm for different quantum of data. The efficiency of these algorithms are analyzed using various statistical indicators like Root mean square error (RMSE), Correlation coefficient (R), Mean absolute error (MAE) and coefficient of determination (R2). The results suggest that the performance of three algorithms is similar irrespective of the quantum of the dataset. The estimated Weibull parameters are almost similar for short term and long term dataset. There is a marginal variation in the obtained parameters when only single year’s wind data is considered for the analysis. The Weibull probability distribution curve fits very well on the wind speed histogram when only single year’s wind speed data is considered and fits marginally well when short term and long term wind speed data is considered

scholarly journals Wind Speed Distribution in Ninava Governorate

2009 ◽  
Vol 22 (4) ◽  
pp. 56-74
Author(s):  
Waleed Al-Rijabo ◽  
Lamia fayik ◽  
Bashar Jaro
Keyword(s):  
Wind Speed ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals Modeling of Mineral Dust Emissions with a Weibull Wind Speed Distribution Including Subgrid-Scale Orography Variance

2018 ◽  
Vol 35 (6) ◽  
pp. 1221-1236
Author(s):  
Laurent Menut
Keyword(s):  
Wind Speed ◽  
Mineral Dust ◽  
Aerosol Size Distribution ◽  
Test Case ◽  
Subgrid Scale ◽  
Dust Emissions ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Main Driver ◽  
Dust Modeling

AbstractThe modeling of mineral dust emissions requires an extensive knowledge of the wind speed close to the surface. In regional and global models, Weibull distributions are often used to better represent the subgrid-scale variability of the wind speed. This distribution mainly depends on a k parameter, itself currently parameterized as a function of the wind speed value. In this study we propose to add the potential impact of the orography variance in the wind speed distribution by changing the k parameter value. Academic test cases are designed to estimate the parameters of the scheme. A realistic test case is performed over a large domain encompassing the northern part of Africa and Europe and for the period 1 January–1 May 2012. The results of the simulations are compared to particulate matter (PM10) surface concentrations and Aerosol Robotic Network (AERONET) aerosol optical depth and aerosol size distribution. We show that with the orography variance, the simulation results are closer to the ones without variance, showing that this additional variability is not the main driver of possible errors in mineral dust modeling.

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.

Wind Energy Potential in Nine Coastal Sites in Malaysia

2013 ◽  
Vol 1 (1) ◽  
pp. 10-15
Author(s):  
Kamaruzzaman Sopian ◽  
Tamer Khatib
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Data Bank ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Speed Distribution ◽  
Average Wind ◽  
Wind Speed Distribution ◽  
Wind Energy Potential ◽  
Swept Area

 In this paper, the wind energy potential in Malaysia is examined by analyzing hourly wind speed data for nine coastal sites namely Bintulu, Kota Kinabalu, Kuala Terengganu, Kuching, Kudat, Mersing, Sandakan, Tawau and Pulau Langkawi. The monthly averages of wind speed and wind energy are calculated. Moreover, the wind speed distribution histogram is constructed for these sites. The results showed that the average wind speed for these sites is in the range of (1.8-2.9) m/s while the annual energy of the wind hitting a wind turbine with a 1 m2 swept area is in the range of (15.4-25.2) kWh/m2.annum. This paper provides a data bank for wind energy for Malaysia.

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