To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of mode decomposition and new algorithm called Membrane Computing Optimization (MCO) is introduced for wind speed forecasting. Compared with existing wind speed forecasting methods, this proposed model has improved the prediction accuracy. The proposed model involves three main steps:decomposing the original wind speed series into several Intrinsic Mode Functions (IMFs) via Complementary Ensemble Empirical Mode Decomposition (CEEMD) for simplifying the complex data, individually predicting each IMF with Support Vector Regression (SVR) optimized by MCO, and integrating all predicted IMFs for the ensemble result as the final prediction. Nine benchmark models, including single support vector regression models, different decomposition methods and models with the same decomposition method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid model is remarkably superior to all considered benchmark models for its higher prediction accuracy.