LOCAL GENERAL REGRESSION NEURAL NETWORK FOR PREDICTION OF WIND POWER

Wind power is considered one of the most rapidly growing sources of electricity generation all over the world. This paper proposes a new approach for wind power prediction. The proposed method is derived by integrating the kernel principal component analysis (KPCA) method with the general regression neural network (GRNN) and local prediction framework. Local prediction uses only a set of K nearest neighbors in the reconstructed embedded space with considering the more relevant historical instances. In the proposed method, the first stage is using KPCA to extract features and obtain kernel principal components which used to construct the phase space of the time series of input. Then, local GRNN (LGRNN) is employed in the second stage to solve the wind power prediction problem. The proposed method is evaluated using real world dataset. The results show that the proposed method provides a much better prediction performance in comparison with other published methods employing the same data.

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