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JEET, Vol. 13, No. 5, September 2018
Short-term Wind Power Prediction Based on Empirical Mode Decomposition and Improved Extreme Learning Machine
Zhongda Tian
Area A - Electric Power Engineering
Abstract For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.
Keyword Short-term wind power,Prediction,Empirical mode decomposition,Improved extreme learning machine.
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