Abstract: When the current method is used to predict the load of the power system of the distribution network, because the missing value interpolation processing of the power data is not performed before the power load prediction, the method has poor prediction accuracy, long prediction time. For the problem of poor forecasting performance, a research on the load forecasting of the distribution network power system based on the DE-ELM (Differential Evolution-Extreme Learning Machine) algorithm is proposed. This method first denoises the power data according to the wavelet transform method, completes the interpolation of the missing values of the power data according to the denoising results, and obtains a complete power data set; then divides the data set into two parts: a training set and a test set. The optimization method introduces the extreme learning machine, uses the DE-ELM algorithm to calculate the training set, builds a network model based on the results. Finally puts the test set into the constructed model for training, and realizes the load forecast of the distribution network power system based on the output results. The experimental results show that when the method is used to forecast the load of the distribution network power system, the forecasting accuracy is high, the forecasting time is short, and the forecasting performance is good.

Key words: differential evolution-extreme learning machine(DE-ELM) algorithm,  , distribution network,  , power system,  , load forecasting,  , forecasting method