Aiming at the problem of poor accuracy in short-term load forecasting of distribution network lines

this paper proposes a long short-term neural network load forecasting method based on clustering and trend indicators. First

the K-Means clustering method is used to classify the distribution network stations with similar load characteristics. Then

on the basis of each cluster of load data the seasonal trend change index from historical load data is calculated as the input feature of load prediction model. Besides

the long short-term neural network model that can transmit time series information is established to train and predict each cluster load. Finally

the prediction results of each cluster load are superimposed to obtain the total line load prediction results. Based on the load data of a certain line in Hunan

the line load changes at 96 points in the feature are predicted. It is verified that the method proposed can deeply excavate the characteristics and changing trends of power load

improving the short-term prediction accuracy of distribution network line load.