Wind energy resources are a clean renewable energy source. Accurate prediction of wind speed has important theoretical significance and practical value for sustainable use of wind energy, energy planning, and economic development. This paper proposes a hybrid model for prediction of wind speed using support vector machine and genetic programming models, with error compensation for the prediction residuals. Wind speed and the residuals generated using the hybrid model are simulated and predicted. The proposed model is applied to monthly average wind speed data observed over the period January 2005–December 2014 from the Tuoli and Hetian stations in Xinjiang, China. For each time series, eight prediction schemes, including independent support vector machine model and genetic programming model and their combination, were tried. At the same time, phase space reconstruction method was used to select model input factors. The results clearly show the performance differences of the eight wind speed prediction schemes in the prediction effect of the two time series. The combination of the support vector machine wind speed prediction model and the genetic programming residual prediction model has the best prediction effect, with a correlation coefficient value above 0.9, mean square value below 0.1, and mean absolute error value below 0.2. This combined model is found to be quite efficient and, therefore, the proposed approach is highly suitable to predict the monthly average wind speed.