基于注意力机制的电力负荷预测方法研究OACSTPCD

Research on power load forecasting methods based on attention mechanism,analyze the operation rules of the power grid,and ensure the reliable operation of the power grid.Establish the power load data prediction model,take the historical power load data as the input data of the input layer,reduce the dimension of the input data through the convolution neural network with double convolution layer and double pooling layer structure,extract the feature vector of the power load data,use the GRU layer to learn the extracted feature vector,and obtain the change rule of the power load data,based on which,use the attention mechanism to assign different weights to the power load data,ensure the convenience of the power data prediction model to obtain the long-distance dependence features in the sequence,and output the power data prediction results through the output layer to complete the efficient analysis of power data.The experimental results show that this method can improve the autocorrelation of power data characteristics,and effectively select power load data by assigning reasonable attention mechanism weights to power load data;Accurate prediction of power load data of multiple substations can be realized through power data analysis.