Abstract
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.
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