Wind power prediction using cluster based ensemble regression

Sumaira Tasnim, Ashfaqur Rahman*, Amanullah Maung Than Oo, Md Enamul Haque

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Accurate prediction of wind power is of vital importance for demand management. In this paper, we adopt a cluster-based ensemble framework to predict wind power. Natural groups/clusters exist in datasets and learning algorithms benefit from group/cluster wise learning - a philosophy that is not well explored for wind power prediction. The research presented in this paper investigates this philosophy to predict wind power by using an ensemble of regression models on natural clusters within wind data. We have conducted a series of experiments on a large number of locations across Australia and analyzed the existence of clusters within wind data, suitability of linear and nonlinear regression models for the proposed framework, and how well the cluster-based ensemble performs against the situation when no clustering is done. Experimental results demonstrate prediction improvement as high as 17.94% through the usage of the cluster-based ensemble regression algorithm.

Original languageEnglish
Article number1750026
Pages (from-to)1750026-1-1750026-15
Number of pages15
JournalInternational Journal of Computational Intelligence and Applications
Issue number4
Publication statusPublished - Dec 2017
Externally publishedYes


  • Renewable energy
  • wind power forecasting
  • smart grid


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