Load Prediction Study of Optimized BP Based on GCA-MVO-ICA
To enhance the accuracy of microgrid load forecasting,a method is proposed that utilizes grey correlation analysis to optimize the input layer data of the BP neural network,employs a novel multidimen-sional universe algorithm to optimize the hidden layer weights,and utilizes an imperial competitive algorithm to optimize the output layer results in a layer-by-layer optimization model.The method is applied to the analysis of two sets of actual measurement data.The results indicate that the proposed GCA-MVO-ICA opti-mization method for the BP network can improve the prediction accuracy of microgrid load and exhibits good universality.
BP neural networkgray correlationmultiverse algorithmempire competition algorithmload forecasting
万方数据
维普
