Hybrid Wind Speed Prediction Model Using Intrinsic Mode Function (IMF) and Gradient Boosted Machine (GBM)


  • S. M. Lawan Kano University of Science and Technology
  • W. A. W. Z. Abidin Universiti Malaysia Sarawak
  • M. Alhaji Universiti Malaysia Sarawak
  • M. K. Hasan Universiti Malaysia Sarawak
  • T. Masri Universiti Malaysia Sarawak


Gradient Boosted Machine (GBM), instinct mode function (IMF), prediction, Sarawak, wind speed


Before sitting a wind turbine, reliable wind speed prediction is prerequisite requirements that must be performed in order to get optimum energy yield. Single model has a lot of constraints in terms of prediction accuracy, to solve this persistent problem, this paper presents the application of hybrid model based on IMF and GBM so as to predict the wind speed in the areas with limited or absent of data. In the first place, the observed wind speed was decomposed into six using IMF in order to reduce ill-define stochastic nature of wind speed. The decomposed wind speed was used to train, test and validate the model developed GMB model which was developed in a Matlab environment. The final predicted values are obtained by summing all the individual prediction sub models. Wind speed data observed in the existing wind stations in Sarawak for a period of 1 year from 2017 to 2018 were used for the simulation. The model implementation confirmed that the proposed model is robust and capable to predict wind speed in remote and rural areas. A comparison with conventional method (ARIMA) was further investigated, the results showed the superiority of the new hybrid model over ARIMA.


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How to Cite

Lawan, S. M. ., Abidin, W. A. W. Z. ., Alhaji, M. ., Hasan, M. K. ., & Masri, T. . (2020). Hybrid Wind Speed Prediction Model Using Intrinsic Mode Function (IMF) and Gradient Boosted Machine (GBM). International Journal of Integrated Engineering, 12(6), 128–136. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/6393