Corrigendum to “Weibull parameters estimation using combined energy pattern and power density method for wind resource assessment”

This work deals with the development of Combined Energy Pattern & Power Density Method (CEPPDM) to evaluate the two parameters needed to define Weibull distribution. Five years (2015–2019) wind data recorded each 60-minutes interval at eleven representative sites in Pakistan was used and efficiency of CEPPDM was compared with Energy Pattern Factor Method (EPFM) and Power Density Method (PDM) with the help of MAPE, MSE and R2. Analysis showed that CEPPDM is the most efficient method while EPFM is the least efficient. Furthermore, it was found that RYK is the most lucrative site and Layyah is the weakest site regarding wind potential. Wind rose plots were drawn which showed that the wind mainly blows in the range of 200°–270°.

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Efficiency comparison of historical and newly developed Weibull parameters estimation methods

Energy Exploration & Exploitation ◽

10.1177/0144598720959758 ◽

2020 ◽

pp. 014459872095975

Author(s):

Muhammad Sumair ◽

Tauseef Aized ◽

Syed Asad Raza Gardezi ◽

Muhammad Waqas Aslam

Keyword(s):

Accurate Method ◽

Resource Assessment ◽

Empirical Method ◽

Parameters Estimation ◽

Estimation Methods ◽

Weibull Parameters ◽

Wind Resource Assessment ◽

Performance Evaluation Criterion ◽

Efficiency Comparison ◽

Wind Resource

This work compares the efficiency of historically used Weibull parameters estimation methods with newly developed method. Newly developed method has been termed as Combined Linearized Moment Method (CLMM). Five-year wind data at five locations namely Chaghi, Lehri, Badin, Hyderabad and Nankana Sahib (Pakistan) was used for the calculation of Weibull parameters. Efficiency was assessed and compared using R-Squared(R2), MSE, RMSE, wind error (WE), MAPE and Chi-test ([Formula: see text]). Each method was given a rank against each performance evaluation criterion and then an overall ranking was done. The study concluded that CLMM is the most accurate method among all while Empirical Method of Justus (EMJ) is the least accurate. Hence, CLMM can be used to estimate Weibull parameters for wind resource assessment with significant accuracy.

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Bayesian Method for Estimating Weibull Parameters for Wind Resource Assessment in the Equatorial Region: a Comparison Between Two-parameter and Three-parameter Weibull Distributions

10.21203/rs.3.rs-504670/v1 ◽

2021 ◽

Author(s):

M. G. M. Khan ◽

M. Rafiuddin Ahmed

Keyword(s):

Weibull Distribution ◽

Bayesian Approach ◽

Resource Assessment ◽

Equatorial Region ◽

Weibull Parameters ◽

Wind Speeds ◽

Wind Resource Assessment ◽

Low Wind Speeds ◽

Wind Resource ◽

Two Parameter

Abstract The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to accurately characterize wind speed at every location, especially at sites where the frequency of low speed is high, such as the Equatorial region. In this work, for the robustness in wind resource assessment, we first propose a Bayesian approach in estimating Weibull parameters. Secondly, we compare the techniques of wind resource assessment using both two and three-parameter Weibull distributions for different sites in the Equatorial region. The Bayesian inference approach is adopted using Markov Chain Monte Carlo (MCMC) algorithms. Simulation studies conducted in this research confirms that the Bayesian approach seems to be a new robust alternative technique for accurate estimation of Weibull parameters. An appropriate Weibull distribution and the application of the Bayesian approach in estimating distribution parameters were determined using data from six sites in the Equatorial region from 1° N of Equator to 19° South of Equator. Results revealed that a three-parameter Weibull distribution is a better fit for wind data having a greater percentage of low wind speeds (0-1 m/s) and low skewness. However, wind data with a smaller percentage of low wind speeds and high skewness showed better results using a two-parameter Weibull distribution. The results also demonstrate that the proposed Bayesian approach to estimate Weibull parameters is extremely useful in the analysis of wind power potential, as it provides more accurate results while characterizing lower wind speeds.

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Bayesian method for estimating Weibull parameters for wind resource assessment in the Equatorial region: a comparison between two-parameter and three-parameter Weibull distributions

10.21203/rs.3.rs-504670/v2 ◽

2022 ◽

Author(s):

M. G. M. Khan ◽

M. Rafiuddin Ahmed

Keyword(s):

Weibull Distribution ◽

Bayesian Approach ◽

Resource Assessment ◽

Equatorial Region ◽

Weibull Parameters ◽

Wind Speeds ◽

Wind Resource Assessment ◽

Low Wind Speeds ◽

Wind Resource ◽

Two Parameter

Abstract The two-parameter Weibull distribution has garnered much attention in the assessment of windenergy potential. The estimation of the shape and scale parameters of the distribution has broughtforth a successful tool for the wind energy industry. However, it may be inappropriate to use thetwo-parameter Weibull distribution to assess energy at every location, especially at sites wherelow wind speeds are frequent, such as the Equatorial region. In this work, a robust technique inwind resource assessment using a Bayesian approach for estimating Weibull parameters is firstproposed. Secondly, the wind resource assessment techniques using a two-parameter Weibulldistribution and a three-parameter Weibull distribution which is a generalized form of twoparameterWeibull distribution are compared. Simulation studies confirm that the Bayesianapproach seems a more robust technique for accurate estimation of Weibull parameters. Theresearch is conducted using data from seven sites in Equatorial region from 1o N of Equator to 19oSouth of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shiftparameter is a better fit for wind data having a higher percentage of low wind speeds (0-1 m/s) andlow skewness. However, wind data with a smaller percentage of low wind speeds and highskewness showed better results with a two-parameter distribution that is a special case of threeparameterWeibull distribution with zero shift parameter. The results also demonstrate that theproposed Bayesian approach and application of a three-parameter Weibull distribution areextremely useful in accurate estimate of wind power and annual energy production.

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