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Wind energy assessment considering wind speed correlation in Malaysia

Author

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  • Goh, H.H.
  • Lee, S.W.
  • Chua, Q.S.
  • Goh, K.C.
  • Teo, K.T.K.

Abstract

Renewable energy is the current trend of energy sourcing. Numerous scientists, inventors, and engineers are working hard to harness renewable energy. The application of renewable energy is very wide; it can be as small as lighting an LED bulb or as large as generating the electricity of a town or even a country. Wind energy plays an important role in the context of electricity generation. Wind energy is highly dependent on the wind speed at a wind site. Wind prediction is necessary for a wind energy assessment of a potential wind farm. In this study, the wind energy assessment is based on wind prediction using the Mycielski algorithm and K-means clustering in Kudat, Malaysia. The predicted results are analysed using Weibull analysis to obtain the most probable wind speed. From the results of this study, K-means clustering is more accurate in prediction when compared with the Mycielski algorithm. The most probable wind in Kudat is sufficient to operate the wind turbines.

Suggested Citation

  • Goh, H.H. & Lee, S.W. & Chua, Q.S. & Goh, K.C. & Teo, K.T.K., 2016. "Wind energy assessment considering wind speed correlation in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1389-1400.
    • Handle: RePEc:eee:rensus:v:54:y:2016:i:c:p:1389-1400
    DOI: 10.1016/j.rser.2015.10.076
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