Optimizing of Artificial Neural Network Based on Immune Genetic Algorithm in Power Load Forecasting

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. There are many difficulties in the application of the method of BP neural network, for example, it is difficult to define the network structure and the network is easy to fall into local solution. To overcome these, in this paper, at first, by giving the undefined relation between learning ability and generalization ability of BP neural network, the hidden notes are obtained. Secondly, it poses to optimize the neural network structure and connection weights and defines the original weights and bias by means of genetic algorithm. Meanwhile, it reserves the best individual in evolution process, so that to build up a genetic algorithm neural networks model. This new model has high convergent speed and qualification. In order to prove the rationality of the improving GA-BP model, it analyses the network load with an area. Compare with BP neural network, it can be found that the new model has higher accuracy for power load forecasting.