Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model
改进粒子群—BP神经网络模型的短期电力负荷预测

Aiming at improving the power short-term forecast accuracy and speed, the Modified Particle Swarm Optimizer (MPSO) algorithm was presented. The forecast model was set up by combining with the Back Propagation (BP) neural network to form Modified Particle Swarm Optimizer and Back Propagation (MPSO-BP) neural network algorithm, and then the neural network was trained by using the MPSO-BP algorithm. It can automatically determine the parameters of the neural network from the sample data. The power short-term forecast model based on the MPSO-BP neural network was formed with considering weather, date and other factors. The experimental results show that the MPSO-BP algorithm improves the BP neural network generalization capacity, and the convergence of method is faster and forecast accuracy is more accurate than that of the traditional BP neural network. Therefore, the model can be used to forecast the short-term load of the power system.