A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing, Malaysia

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R2 determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m2, and SSW is the most common wind direction for this data.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalInternational Journal of Green Energy
DOIs
Publication statusAccepted/In press - 24 Sep 2017

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Wind power
Weibull distribution
Freight transportation
Sensors

Keywords

  • Finite mixture of the von Mises distribution
  • Weibull distribution
  • wind direction
  • wind power
  • wind speed

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

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title = "A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing, Malaysia",
abstract = "Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R2 determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m2, and SSW is the most common wind direction for this data.",
keywords = "Finite mixture of the von Mises distribution, Weibull distribution, wind direction, wind power, wind speed",
author = "Nortazi Sanusi and Azami Zaharim and Sohif Mat and Kamaruzzaman Sopian",
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AU - Sanusi, Nortazi

AU - Zaharim, Azami

AU - Mat, Sohif

AU - Sopian, Kamaruzzaman

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N2 - Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R2 determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m2, and SSW is the most common wind direction for this data.

AB - Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R2 determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m2, and SSW is the most common wind direction for this data.

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