Electricity price forecast based on PSO-BP neural network

In order to improve the problem in BP neural network of electricity price forecast at present which is sensitive with the initial weights,easy to fall into the local least value and have slow convergence speed etc.,the particle swarm optimization(PSO) algorithm based on the random global optimization is inducted into the network training;the particle swarm optimization algorithm is used for glancing study in order to confirm the initial values;then the neural network is used for given accuracy to found the PSO-BP neural network model.Comparing to the traditional BP neural network and PSO generalized regression neural network,the PSO-BP neural network model has the merits of faster convergence,needing fewer historical data,higher forecast precision etc.;and it can be used in the short-term electricity price forecast of electric power systems.