Abstract
Short-term wind speed forecasting plays an increasingly important role in the security, scheduling, and optimization of power systems. As wind speed signals are usually nonlinear and nonstationary, how to accurately forecast future states is a challenge for existing methods. In this paper, for highly complex wind speed signals, we propose a multiple kernel learning- (MKL-) based method to adaptively assign the weights of multiple prediction functions, which extends conventional wind speed forecasting methods using a support vector machine. First, empirical mode decomposition (EMD) is used to decompose complex signals into several intrinsic mode function component signals with different time scales. Then, for each channel, one multiple kernel model is constructed for forecasting the current sequence signal. Finally, several experiments are carried out on different New Zealand wind farm data, and the relevant prediction accuracy indexes and confidence intervals are evaluated. Extensive experimental results show that, compared with existing machine learning methods, the EMD-MKL model proposed in this paper has better performance in terms of the prediction accuracy evaluation indexes and confidence intervals and shows a better ability to generalize.
Highlights
Energy is crucial to social and economic development all over the world
The adaptive choice of optimal kernels is introduced according to the current testing case, which refers to linear combination techniques on base kernels, called multiple kernel learning (MKL) [35]
We use the open wind speed datasets of a wind farm in New Zealand to verify the validity and reliability of the novel empirical mode decomposition (EMD)-MKL model. e data are from an organization called NIWA, published in the public report in [43]
Summary
A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning. As wind speed signals are usually nonlinear and nonstationary, how to accurately forecast future states is a challenge for existing methods. For highly complex wind speed signals, we propose a multiple kernel learning- (MKL-) based method to adaptively assign the weights of multiple prediction functions, which extends conventional wind speed forecasting methods using a support vector machine. Several experiments are carried out on different New Zealand wind farm data, and the relevant prediction accuracy indexes and confidence intervals are evaluated. Extensive experimental results show that, compared with existing machine learning methods, the EMD-MKL model proposed in this paper has better performance in terms of the prediction accuracy evaluation indexes and confidence intervals and shows a better ability to generalize
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