LAUSR

To study the power load forecasting optimization of general regression neural network using particle swarm optimization. Refer to the domestic and foreign literature, The paper refers to domestic and foreign literature, by comparing the GRNN with BP, MDE with DE based on particle swarm optimization, it compares the test result and studies the power load forecasting of the general regression neural network. The maximum error rate of the particle swarm optimization is lower than 9%, and the minimum error rate is approximately 0, the forecasting accuracy rate of general regression neural network is high. the optimization method has good convergence and is highly accurate, and could be applied to short-term power load forecasting.