Load Prediction Based on Improved BP Neural Network
Reasonable use of power generation resources is a way to reduce the waste of resources,according to the characteristics of electric energy cannot be stored in large quantities,it is necessary to make a reasonable prediction of the power generation in the future period of time.Taking BP neural network as the research object,aiming at its characteristics that it is easy to fall into local op-timal,genetic algorithm and particle swarm optimization are used to optimize BP neural network respectively,and BP neural net-work time series prediction model is established.This paper compares the fit degree and error of the optimized neural network of two population intelligent algorithms for the load prediction of the power system,and obtains the results through Matlab software ex-periment.The analysis results show that the BP neural network optimized by genetic algorithm and particle swarm optimization can reduce the prediction error.
BP neural networkgenetic algorithmalgorithm optimizationparticle swarm optimization
NETL
NSTL
万方数据
