Optimizing of BP Neural Network based on genetic algorithms in power load forecasting

Abstract

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. In this paper, For overcoming difficulties in application of the method of BP neural network, such as it is difficult to define the network structure and the network is easy to fall into local solution. 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 algorithms 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 a area. Compare with BP neural network, it can be found that the new model has higher accuracy for power load forecasting.