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A time series ensemble method to predict wind power

Tasnim, S., Rahman, A., Shafiullah, GM., Oo, A.M.T. and Stojcevski, A. (2014) A time series ensemble method to predict wind power. In: IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG) 2014, 9 - 12 Dec. 2014, Orlando, FL

Link to Published Version: http://dx.doi.org/10.1109/CIASG.2014.7011544
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Abstract

Wind power prediction refers to an approximation of the probable production of wind turbines in the near future. We present a time series ensemble framework to predict wind power. Time series wind data is transformed using a number of complementary methods. Wind power is predicted on each transformed feature space. Predictions are aggregated using a neural network at a second stage. The proposed framework is validated on wind data obtained from ten different locations across Australia. Experimental results demonstrate that the ensemble predictor performs better than the base predictors.

Publication Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/31738
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