Combination Forecasting Model of Short-Term Wind Speed for Wind Farm

In order to improve the prediction accuracy of short-term wind speed, a combination forecasting model based on support vector machine(SVM)with variable-weight coefficient was presented. The support vector machine with different kernel parameters was chosen as the single forecasting model to guarantee the difference between single models. The selection of kernel parameters was optimized using particle swarm algorithm to ensure the accuracy of each individual model. The combination forecasting method satisfying the variable weight coefficient with the least square sum of forecasting errors was used to calculate the weight coefficient of the each individual model at different time of wind speed forecast. The simulation experimental results show that the proposed combination forecasting model with variable weight coefficient has better forecast result for short term wind speed prediction, and the forecast accuracy is higher than the each single model and combination model with fixed-weight coefficient. © 2017, Editorial Board of Acta Energiae Solaris Sinica. All right reserved.