Rough Neuron Based RBF Neural Networks for Short-Term Load Forecasting

Short-term load in power system is nonlinear and non-stationary. To cope with the problem that the training error of neural network prediction model is increased while the generalization ability is reduced caused by the large input fluctuation, the rough neurons with upper and lower inputs are introduced into the radial basis function (RBF) neural networks, a power system short-term load forecasting model based on rough RBF neural networks is presented. The genetic algorithm is used to optimize related parameters of rough neural networks to find the best parameters in the global. The simulation results show that the proposed method can effectively improve the prediction accuracy of the power system short-term load.