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Research Article
Zhiming Wang
Lanzhou University of Technology
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters, based on expectation-maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as wind direction and relation circular variable models, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-minute field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.
Keywords
Finite Mixture Statistical Distributions, Angular-linear Modeling, Weibull Mixture Distribution, Wide Wind Turbine Applications
The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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View the published article at Scientific Reports.
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Editorial decision: Major revision
On 05 Jul, 2021
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Received 18 Jun, 2021
Reviewers agreed
On 17 Jun, 2021
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Invitations sent on 17 Jun, 2021
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Editor invited
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A new preprint design is coming!
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See the published version of this preprint at Scientific Reports.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Zhiming Wang
Lanzhou University of Technology
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Scientific Reports.
Version 1
Posted 15 Jul, 2021
No community comments so far
Editorial decision: Major revision
On 05 Jul, 2021
Reviews received
Received 18 Jun, 2021
Reviewers agreed
On 17 Jun, 2021
Reviewers invited
Invitations sent on 17 Jun, 2021
Editor assigned
On 08 Jun, 2021
Editor invited
On 08 Jun, 2021
Submission checks complete
On 08 Jun, 2021
First submitted
On 05 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Scientific Reports.
Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters, based on expectation-maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as wind direction and relation circular variable models, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-minute field monitoring wind data and compared with the other estimation methods and judged by the values of R2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.
The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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