Abstract
Wind power is considered one of the most rapidly growing sources of electricity generation all over the world. This study proposes a new approach for wind power prediction. The proposed method is derived by integrating the kernel principal component analysis (KPCA) method with the evolutionary optimised general regression neural network (GRNN) and local prediction framework. Local prediction uses only a set of K nearest neighbours in the reconstructed embedded space with considering the more relevant historical instances. In the evolutionary optimised local GRNN (EOLGRNN), the kernel bandwidth (smooth parameter) that controls the smoothness of the approximation is coded in a chromosome and determined by the optimisation using evolutionary algorithm. In the proposed method, KPCA is used in the first stage to extract features and obtain kernel principal components which used to construct the phase space of the time series of input. Then, EOLGRNN 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.