On spatial analysis of wind energy potential in Malaysia

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14 Citations (Scopus)

Abstract

Statistical distribution for describing the wind speed at a particular location provides information about the wind energy potential. In this paper, nine different statistical distributions are fitted to the data of average hourly wind speed for 60 wind stations across the west and east Malaysia from the year 2000 to 2009. The distributions found to be most adequate for describing the wind speed at each particular station are determined based on several goodness of fits criteria. The spatial dependence in the data is investigated by making use of the semivariogram involving the expected speed found using the identified distribution for each individual station. Since the spatial dependence in the data of Peninsular Malaysia could not be described well by the semivariogram, the inverse distance weighting method is used for describing the spatial distribution of wind speed. For the data of East Malaysia, however, the power semivariogram is found to be fitted quite well. Accordingly, the kriging method is applied for spatial prediction. It is found that, the regions in the northeast, northwest and southeast of the peninsula have a good potential for wind energy. For East Malaysia, the northeast and southwest regions of Sarawak are found to be the most potential.

Original languageEnglish
Pages (from-to)467-477
Number of pages11
JournalWSEAS Transactions on Mathematics
Volume11
Issue number6
Publication statusPublished - Jun 2012

Fingerprint

Wind Energy
Spatial Analysis
Malaysia
Wind Speed
Semivariogram
Wind power
Spatial Dependence
Statistical Distribution
Statistical Distributions
Spatial Prediction
Kriging
Goodness of fit
Spatial Distribution
Weighting
Spatial distribution
Spatial analysis
Wind energy

Keywords

  • Inverse distance weighting method
  • Kriging
  • Semivariogram
  • Spatial estimation
  • Wind energy
  • Wind speed distribution

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

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title = "On spatial analysis of wind energy potential in Malaysia",
abstract = "Statistical distribution for describing the wind speed at a particular location provides information about the wind energy potential. In this paper, nine different statistical distributions are fitted to the data of average hourly wind speed for 60 wind stations across the west and east Malaysia from the year 2000 to 2009. The distributions found to be most adequate for describing the wind speed at each particular station are determined based on several goodness of fits criteria. The spatial dependence in the data is investigated by making use of the semivariogram involving the expected speed found using the identified distribution for each individual station. Since the spatial dependence in the data of Peninsular Malaysia could not be described well by the semivariogram, the inverse distance weighting method is used for describing the spatial distribution of wind speed. For the data of East Malaysia, however, the power semivariogram is found to be fitted quite well. Accordingly, the kriging method is applied for spatial prediction. It is found that, the regions in the northeast, northwest and southeast of the peninsula have a good potential for wind energy. For East Malaysia, the northeast and southwest regions of Sarawak are found to be the most potential.",
keywords = "Inverse distance weighting method, Kriging, Semivariogram, Spatial estimation, Wind energy, Wind speed distribution",
author = "Nurulkamal Masseran and Razali, {Ahmad Mahir} and Kamarulzaman Ibrahim and {Wan Zin @ Wan Ibrahim}, {Wan Zawiah} and Azami Zaharim",
year = "2012",
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T1 - On spatial analysis of wind energy potential in Malaysia

AU - Masseran, Nurulkamal

AU - Razali, Ahmad Mahir

AU - Ibrahim, Kamarulzaman

AU - Wan Zin @ Wan Ibrahim, Wan Zawiah

AU - Zaharim, Azami

PY - 2012/6

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N2 - Statistical distribution for describing the wind speed at a particular location provides information about the wind energy potential. In this paper, nine different statistical distributions are fitted to the data of average hourly wind speed for 60 wind stations across the west and east Malaysia from the year 2000 to 2009. The distributions found to be most adequate for describing the wind speed at each particular station are determined based on several goodness of fits criteria. The spatial dependence in the data is investigated by making use of the semivariogram involving the expected speed found using the identified distribution for each individual station. Since the spatial dependence in the data of Peninsular Malaysia could not be described well by the semivariogram, the inverse distance weighting method is used for describing the spatial distribution of wind speed. For the data of East Malaysia, however, the power semivariogram is found to be fitted quite well. Accordingly, the kriging method is applied for spatial prediction. It is found that, the regions in the northeast, northwest and southeast of the peninsula have a good potential for wind energy. For East Malaysia, the northeast and southwest regions of Sarawak are found to be the most potential.

AB - Statistical distribution for describing the wind speed at a particular location provides information about the wind energy potential. In this paper, nine different statistical distributions are fitted to the data of average hourly wind speed for 60 wind stations across the west and east Malaysia from the year 2000 to 2009. The distributions found to be most adequate for describing the wind speed at each particular station are determined based on several goodness of fits criteria. The spatial dependence in the data is investigated by making use of the semivariogram involving the expected speed found using the identified distribution for each individual station. Since the spatial dependence in the data of Peninsular Malaysia could not be described well by the semivariogram, the inverse distance weighting method is used for describing the spatial distribution of wind speed. For the data of East Malaysia, however, the power semivariogram is found to be fitted quite well. Accordingly, the kriging method is applied for spatial prediction. It is found that, the regions in the northeast, northwest and southeast of the peninsula have a good potential for wind energy. For East Malaysia, the northeast and southwest regions of Sarawak are found to be the most potential.

KW - Inverse distance weighting method

KW - Kriging

KW - Semivariogram

KW - Spatial estimation

KW - Wind energy

KW - Wind speed distribution

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