Assessing wind energy potential using finite mixture distributions

Koca, Melih Burak; Kılıç, Muhammet Burak; Şahin, Yusuf

doi:10.3906/elk-1802-109

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…where k, the shape parameter and c, the scale parameter, are taken as indicators of the data dispersion and the average speed, respectively. The Weibull parameters can be obtained through approximated relationships (detailed in the Appendix A (A8)) obtained using the Method of Moments (MOM) [54]. The differences in the PDF of the observed and modelled time series were assessed using the confidence intervals of the observed parameters.…”

Section: Pdf-based Validationmentioning

confidence: 99%

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

Zekeik,

OrtizBevia,

Alvarez-Garcia

et al. 2024

JMSE

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Offshore wind energy is a promising resource for renewable energy development. Reanalysed wind data are unmatched by other wind data sources in providing a long-term assessment of wind power potential. In this study, 10 of the selected offshore locations close to the Moroccan coast were used to evaluate the ERA5 wind reanalysis dataset against the IFREMER-blended observational dataset covering the years 1993–2016. The ERA5 wind data’s capacity to represent wind variability in the area was confirmed by the results of the statistical methodologies used. All the reanalysed data scored better at capturing the observed wind variability at the southern sites than at the northern ones, where the wind variability was more complex. In a long-term evaluation from 1981 to 2020, the wind power potential in the Moroccan Atlantic coast was found to be very stable except in the northern sites and between Agadir and Bou Arich. Seven of the 10 sites considered were ranked as promising sites for offshore wind power generation, with wind power densities above 420 W/m2 at 100 m in height. Additionally, the change in signs in the variability toward the middle of the ERA5 record, which was seen at all locations and was also evident in the observations, did not significantly affect the yearly wind power density. However, the seasonal distribution of the latter was modified according to the local features of the seasonal variability.

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Section: Pdf-based Validationmentioning

confidence: 99%

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

Zekeik,

OrtizBevia,

Alvarez-Garcia

et al. 2024

JMSE

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“…( 20)-( 25) are ML estimations with respect to parameters of interest, so we can obtain the explicit solutions of log-likelihood equations for the parameters the usage the numerical methods such as conjugate gradients algorithm, Nelder-Mead, Brent-Dekker algorithm, quasi-Newton, and simulated annealing algorithm. The authors in this research to find the MLEs of the unknown parameters selected Nelder-Mead numerical method as an optimization algorithm [36]. Table 2 indicates the estimated results of the parameters for WE3, LL3, LN3, GEV, WE3-LL3, and LL3-WE3 distributions via the MLE method.…”

Section: Parameters Estimationmentioning

confidence: 99%

The Suitable Statistical Model Selection for the Wind Speed of Tabriz and Orumiyeh Stations

2021

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Wind speed probabilistic distributions are one of the main wind characteristics for the evaluation of wind energy potential in a specific region. In this paper, 3-parameter Log-Logistic distribution is introduced and it compared with six used statistical models for the modeling the actual wind speed data reported of Tabriz and Orumiyeh stations in Iran. The maximum likelihood estimators method via Nelder-Mead algorithm is utilized for estimating the model parameters. The flexibility of proposed distributions is measured according to the coefficient of determination, Chi-square test, Kolmogorov-Smirnov test, and root mean square error criterion. Results of the analysis show that 3-parameter Log-Logistic distribution provides the best fit to model the annual and seasonal wind speed data in Orumiyeh station and except summer season for Tabriz station. Also, wind power density error is estimated for the proposed different distributions.

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“…However, the wind turbines and installation costs are high, therefore the wind energy potential of a region should be carefully estimated to determine the proper turbine type. Wind speed is the key factor in determining the wind energy potential of a region [1][2][3][4]. Statistical distributions are used to model wind speed and estimate energy potential.…”

Section: Introductionmentioning

confidence: 99%

“…Statistical distributions are used to model wind speed and estimate energy potential. The Weibull distribution is one of the most commonly used distributions in wind energy studies due to its simplicity and flexibility [1,2,[4][5][6][7][8][9][10]. There are various techniques used in Weibull parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Using genetic algorithms for estimating Weibull parameters with application to wind speed

Koca

Kılıç

Şahin

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Renewable energy has become a prominent subject for researchers since fossil fuel reserves have been decreasing and are not promising to meet the energy demand of the future. Wind takes an important place in renewable energy resources and there is extensive research on wind speed modeling. Herein, one of the most commonly used distributions for wind speed modeling is the Weibull distribution with its simplicity and flexibility. Maximum likelihood (ML) method is the most frequently used technique in Weibull parameter estimation. Iterative techniques such as Newton-Raphson (NR) use random initial values to obtain the ML estimators of the parameters of the Weibull distribution. Therefore, the success of the iterative techniques highly depends on the initial value selection. In order to deliver a solution to the initial value problem, genetic algorithm (GA) is considered to obtain the estimators of the model parameters. The ML estimators obtained using the GA and NR techniques are compared with the method of moments (MoM) estimators via Monte Carlo simulation and wind speed applications. The results show that the ML estimators obtained using GA present superiority over MoM and the ML estimators obtained using NR.

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Assessing wind energy potential using finite mixture distributions

Cited by 5 publications

References 35 publications

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

Long-Term Assessment of Morocco’s Offshore Wind Energy Potential Using ERA5 and IFREMER Wind Data

The Suitable Statistical Model Selection for the Wind Speed of Tabriz and Orumiyeh Stations

Using genetic algorithms for estimating Weibull parameters with application to wind speed

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