Abstract:Nowadays, with a higher proportion of wind power generation, wind power prediction is increasingly demanding. It is, however, not satisfyingly accurate due to the intermittent and uncertain nature of wind energy. In order to reduce the complexity of the prediction model and improve accuracy, we propose a type-2 fuzzy system based on differential evolution and rule reduction in this study. This model provides a pruning strategy for the reduction in type-2 fuzzy rules, based on which and the differential evolution algorithm is adopted to optimize the parameters of the reduced type-2 fuzzy system. Finally, the prediction accuracy of our method is higher than that of the type-1 fuzzy system and the support vector regressor, according to comparison.