A Combined Forecasting Method of Wind Power Capacity with Differential Evolution Algorithm

As wind power is a mature and important renewable energy, wind power capacity forecasting plays an important role in renewable energy generation’s plan, investment and operation. Combined model is an effective load forecasting method; however, how to determine the weights is a hot issue. This paper proposed a combined model with differential evolution optimizing weights. The proposed model can improve the performance of each single forecasting model of regression, BPNN and SVM. In order to prove the effectiveness of the proposed model, an application of the China’s wind power capacity was evaluated from 2000 to 2010. The experiment results show that the proposed model gets the maximum mean absolute percentage error (MAPE) value 1.791%, which is better than the results of regression, BPNN and SVM.