A Method for Power System Short-Term Load Forecasting Based on Radial Basis Function Neural Network - Zeng Linsuo; Li Yanling (2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013)
In the daily operation of the power system, short-term load forecasting is of great significance, and it has always been an important research subject. Based on the characteristics of the power system load and radial basis function (RBF) neural network nonlinear identification function, this paper uses RBF neural network on power system short-term load forecasting, and using Matlab toolbox to build load forecasting model to predict a maximum daily load in a place. The results of error meet the actual requirements, and it shows that the RBF neural network owns the effectiveness and feasibility in the field of power system short-term load forecasting.