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A Review of Wind Clustering Methods Based on the Wind Speed and Trend in Malaysia

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  • Amar Azhar

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Huzaifa Hashim

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Wind mapping has played a significant role in the selection of wind harvesting areas and engineering objectives. This research aims to find the best clustering method to cluster the wind speed of Malaysia. The wind speed trend of Malaysia is affected by two major monsoons: the southwest and the northeast monsoon. The research found multiple, worldwide studies using various methods to accomplish the clustering of wind speed in multiple wind conditions. The methods used are the k-means method, Ward’s method, hierarchical clustering, trend-based time series data clustering, and Anderberg hierarchical clustering. The clustering methods commonly used by the researchers are the k-means method and Ward’s method. The k-means method has been a popular choice in the clustering of wind speed. Each research study has its objectives and variables to deal with. Consequently, the variables play a significant role in deciding which method is to be used in the studies. The k-means method shortened the clustering time. However, the calculation’s relative error was higher than that of Ward’s method. Therefore, in terms of accuracy, Ward’s method was chosen because of its acceptance of multiple variables, its accuracy, and its acceptable calculation time. The method used in the research plays an important role in the result obtained. There are various aspects that the researcher needs to focus on to decide the best method to be used in predicting the result.

Suggested Citation

  • Amar Azhar & Huzaifa Hashim, 2023. "A Review of Wind Clustering Methods Based on the Wind Speed and Trend in Malaysia," Energies, MDPI, vol. 16(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3388-:d:1121675
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    References listed on IDEAS

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    1. Zhenzhong Zeng & Alan D. Ziegler & Timothy Searchinger & Long Yang & Anping Chen & Kunlu Ju & Shilong Piao & Laurent Z. X. Li & Philippe Ciais & Deliang Chen & Junguo Liu & Cesar Azorin-Molina & Adria, 2019. "A reversal in global terrestrial stilling and its implications for wind energy production," Nature Climate Change, Nature, vol. 9(12), pages 979-985, December.
    2. Masseran, N. & Razali, A.M. & Ibrahim, K. & Wan Zin, W.Z., 2012. "Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia," Energy, Elsevier, vol. 37(1), pages 649-656.
    3. Valliyil Mohammed Aboobacker & Puthuveetil Razak Shanas & Subramanian Veerasingam & Ebrahim M. A. S. Al-Ansari & Fadhil N. Sadooni & Ponnumony Vethamony, 2021. "Long-Term Assessment of Onshore and Offshore Wind Energy Potentials of Qatar," Energies, MDPI, vol. 14(4), pages 1-21, February.
    4. Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
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    Cited by:

    1. Monica Borunda & Adrián Ramírez & Raul Garduno & Carlos García-Beltrán & Rito Mijarez, 2023. "Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data," Energies, MDPI, vol. 16(23), pages 1-34, December.

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