Bivariate probability model for wind power density analysis

Case study

Research output: Contribution to journalArticle

Abstract

The wind power density was investigated in this study to assess the wind energy potential in Kuala Terenganu, Malaysia. The monthly data were statistically analyzed to predict the best distribution that represents bivariate model of wind speed and wind direction. Subsequently, wind power density was assessed by numerical analysis. The results revealed that the estimate mean wind power densities for monthly data are significant with the monsoon seasons in that area. The northeast monsoon effects the East Coast of Peninsular Malaysia, including Kuala Terenganu. Gama distribution together with finite mixture of von Mises is best in represent the monthly bivariate model of wind speed and direction in Kuala Terenganu.

Original languageEnglish
Pages (from-to)656-659
Number of pages4
JournalARPN Journal of Engineering and Applied Sciences
Volume13
Issue number2
Publication statusPublished - 1 Jan 2018

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Wind power
Coastal zones
Numerical analysis

Keywords

  • Finite mixture of von Mises
  • Gama distribution
  • Wind power density

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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title = "Bivariate probability model for wind power density analysis: Case study",
abstract = "The wind power density was investigated in this study to assess the wind energy potential in Kuala Terenganu, Malaysia. The monthly data were statistically analyzed to predict the best distribution that represents bivariate model of wind speed and wind direction. Subsequently, wind power density was assessed by numerical analysis. The results revealed that the estimate mean wind power densities for monthly data are significant with the monsoon seasons in that area. The northeast monsoon effects the East Coast of Peninsular Malaysia, including Kuala Terenganu. Gama distribution together with finite mixture of von Mises is best in represent the monthly bivariate model of wind speed and direction in Kuala Terenganu.",
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N2 - The wind power density was investigated in this study to assess the wind energy potential in Kuala Terenganu, Malaysia. The monthly data were statistically analyzed to predict the best distribution that represents bivariate model of wind speed and wind direction. Subsequently, wind power density was assessed by numerical analysis. The results revealed that the estimate mean wind power densities for monthly data are significant with the monsoon seasons in that area. The northeast monsoon effects the East Coast of Peninsular Malaysia, including Kuala Terenganu. Gama distribution together with finite mixture of von Mises is best in represent the monthly bivariate model of wind speed and direction in Kuala Terenganu.

AB - The wind power density was investigated in this study to assess the wind energy potential in Kuala Terenganu, Malaysia. The monthly data were statistically analyzed to predict the best distribution that represents bivariate model of wind speed and wind direction. Subsequently, wind power density was assessed by numerical analysis. The results revealed that the estimate mean wind power densities for monthly data are significant with the monsoon seasons in that area. The northeast monsoon effects the East Coast of Peninsular Malaysia, including Kuala Terenganu. Gama distribution together with finite mixture of von Mises is best in represent the monthly bivariate model of wind speed and direction in Kuala Terenganu.

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