Load Forecasting Based on Bagging Method and GABP Neural Network

The load forecast is the electric power plan foundation.However,there are lots of disadvantages in the traditional neural network prediction ways,including be sensitive to the initial network weights,easy to run into the local minimum point,etc.Brings forward genetic algorithm to the BP neural network,optimizing the initial network weights.In order to improve the accuracy,use the Bagging method integrated the results.Through the simulation experiment on Matlab,found out that by our research not only the Bagging method and genetic neural network can avoid the disadvantages in the traditional BP network and inherit its good learning and training abilities,but also have stronger generalization ability,and improve the prediction precision.