Short-term load prediction method based on improved IGA-RBF neural network

The invention belongs to the technical field of power system load prediction, and discloses a short-term load prediction method based on an improved IGA-RBF neural network. The method comprises the following steps: constructing an IGA-RBF neural network model for short-term load prediction of a power system; an electric vehicle charging and discharging model is built, and the relation between the charging power of an electric vehicle load and the state of charge of a battery is identified; the load reduction potential of the electric vehicle participating in the demand response is obtained by adjusting the charging power, and the load reduction potential of the single electric vehicle under different influence factors is analyzed; the load of an energy management system of the cluster electric vehicle is predicted, an improved tabu search algorithm is adopted, the optimal load reduction position of the cluster electric vehicle is obtained, and the optimal load reduction capacity of the found node is calculate