The method of short-term load forecasting based on the RBF neural network

Load forecasting is acting an importance role in controlling and running of power system. It´s also the base and premise of power network decision. A more precise forecast not only can strengthen the operation security of power system, also can improve the economy of power system. Electric load is a stochastic non-steady process that consists of many independent stochastic components, but most of factors influencing system load are regular, hence it lays foundation for effective forecast. As a newly-rising intersecting subject, the artificial neural network blazes a new path for revealing the operating mechanism of complicated objects. By making use of strong nonlinear mapping function of RBF neural network model and Expert System to combine previous load data with meteological factors, this Paper makes researches on the short-term electric load forecasting.