Wind Power Short-Term Prediction Based on SVM Trained by Improved FOA

The forecast accuracy of the wind power directly affects the operating cost of the network system,which is directly related to the supply and demand balance of the grid.Therefore,the forecast accuracy of wind power is very important.Considering the prediction accuracy is not high,we propose an improved predictive method that is based on MFOA-SVM.Since penalty factor and kernel parameters of SVM have a great impact on the prediction accuracy,the improved FOA optimizes the parameters of support vector machine and trains model with a good parameter optimization.Finally,the built model is used to the power prediction to evaluate the data.The prediction results show that the improved MFOA-SVM can produce better accuracy for wind power prediction.