Abstract
The wind speed prediction in Kudat, Malaysia had been done by using Mycielski-1 approach and K-mean clustering statistical method. There is some improvement in obtaining the random number of Mycielski-1. Besides, the comparison of K-means clustering with the optimal number of K is presented in this paper. The wind prediction is important to study a favorable site’s wind potential. The prediction is based on 3 years history data provided by Meteorology Department of Malaysia and 1 year data as the reference to check the accuracy of both algorithms. The basic concept of Mycielski-1 algorithm is to predict the next value by looking to history data. Meanwhile, the K-means clustering can group the values with similar mean into the same group, and the prediction can be done by getting the probability of occurrence. The result shows the prediction of Mycielski-1 algorithm and K-means clustering are promising. The wind speed is predicted in order to obtain the mean power for energy planning.
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Acknowledgments
Authors would like to thank the Ministry of Higher Education Malaysia (MOHE) for giving the scholarship to the author in order to accomplish this research. Furthermore, the authors would like to thank Ministry of Energy, Green Technology and Water Malaysia (KeTTHA), Ministry of Higher Education, Malaysia (MOHE) and The Office for Research, Innovation, Commercialization, Consultancy Management (ORICC), UTHM for financially supporting this research under the Fundamental Research Grant Scheme (FRGS) grant No.0905 in funding this research.
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Lee, S.W., Kok, B.C., Goh, K.C., Goh, H.H. (2013). Wind Prediction in Malaysia. In: Zelinka, I., Vasant, P., Barsoum, N. (eds) Power, Control and Optimization. Lecture Notes in Electrical Engineering, vol 239. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00206-4_9
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DOI: https://doi.org/10.1007/978-3-319-00206-4_9
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