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

Weibull parameters for wind speed distribution in Saudi Arabia

Solar Energy ◽  
1994 ◽  
Vol 53 (6) ◽  
pp. 473-479 ◽  
Author(s):  
Shafiqur Rehman ◽  
T.O. Halawani ◽  
Tahir Husain
Keyword(s):  
Saudi Arabia ◽  
Wind Speed ◽  
Weibull Parameters ◽  
Speed Distribution ◽  
Wind Speed Distribution

scholarly journals A Basic Wind Speed Map for Oman

2014 ◽  
Vol 11 (2) ◽  
pp. 64
Author(s):  
A.S. Alnuaimi ◽  
M.A. Mohsin ◽  
K.H. Al-Riyami
Keyword(s):  
Wind Speed ◽  
Return Period ◽  
Structural Design ◽  
Short Term ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Mean Wind Speed

The aim of this research was to develop the first basic wind speed map for Oman. Hourly mean wind speed records from 40 metrological stations were used in the calculation. The period of continuous records ranged from 4–37 years. The maximum monthly hourly mean and the maxima annual hourly mean wind speed data were analysed using the Gumbel and Gringorten methods. Both methods gave close results in determining basic wind speeds, with the Gumbel method giving slightly higher values. Due to a lack of long-term records in some regions of Oman, basic wind speeds were extrapolated for some stations with only short-term records, which were defined as those with only 4– 8 years of continuous records; in these cases, monthly maxima were used to predict the long-term basic wind speeds. Accordingly, a basic wind speed map was developed for a 50-year return period. This map was based on basic wind speeds calculated from actual annual maxima records of 29 stations with at least 9 continuous years of records as well as predicted annual maxima wind speeds for 11 short-term record stations. The basic wind speed values ranged from 16 meters/second (m/s) to 31 m/s. The basic wind speed map developed in this research is recommended for use as a guide for structural design in Oman. 

Summary on Wind Speed Distribution and its Parameter Estimation

2014 ◽  
Vol 986-987 ◽  
pp. 689-693 ◽  
Author(s):  
Hui Li ◽  
Fang Zhang
Keyword(s):  
Parameter Estimation ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Log Normal ◽  
Log Normal Distribution

The main objective of this paper is to review some models of medium-and-long-term wind speed distribution in wind farms, for example Gamma distribution, Log-normal distribution, Weibull distribution, Rayleigh distribution and Burr distribution. On the base of the Weibull distribution, some kinds of parameter estimation approaches are introduced. Meanwhile, the advantages and the disadvantages of various algorithms are analyzed and compared. The prospects of this research are put forward at the end of this paper.

scholarly journals Inverse Weibull Method Application to wind Speed Modeling in Campo Grande-Ms Brazil

2019 ◽  
Vol 6 ◽  
Author(s):  
Razika Ihaddadene ◽  
Nabila Ihaddadene ◽  
Amaury de Souza ◽  
Abdelhadi Beghidja
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Efficient Estimation ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Wind Potential ◽  
Inverse Weibull Distribution

Wind potential estimation requires an analysis of wind characteristics (wind speed density and wind direction). In this study, the applicability of two distribution models named Weibull and Inverse Weibull aiming to characterize the wind speed distribution in Campo Grande-Ms (Brazil) is investigated. The wind speed data collected from Campo Grande-Ms National Institute of Meteorology (INMET) at 10 m height for 5 years from January 2013 to December 2017, at an hour interval, are used. The method of maximum likelihood estimation is applied to calculate the parameters of the selected distributions. The best distribution function is chosen based on three goodness-of-fit statistics, namely; mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R²). The obtained results indicate that the Weibull distribution provides a more accurate and efficient estimation than Inverse Weibull distribution. Therefore, Weibull distribution can be used to better estimate wind speed distribution in Campo Grande-Ms (Brazil) than Inverse Weibull distribution.

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
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