Load forecasting method of power system based on LSTM artificial neural network
CHEN Sheng
LIU Pengfei
WANG Ping
MA Jianwei
Abstract：Aiming at the low accuracy of short-term load forecasting in power market,a combined forecasting model based on LSTM artificial neural network was proposed.The unique advantages of LSTM neural network and its variant GRU neural network in learning time series features during load forecasting were analyzed.Convolution neural network was used as the feature extraction layer of load data in association with GRU network to construct a combined model,and the residual prediction model was established to correct the results.The simulation results show that the prediction effect of neural network with memory function is better than those of ANN and SVM models,and the average relative error of residual prediction model proposed in this work is about 1.79%,and its accuracy is higher than that of single algorithm load forecasting model.
Keywords：load forecastingartificial neural networklong-term and short-term memoryconvolution neural networkaverage relative errorresidual correctionfeature extractioncombined model
Publication Date：2024-01-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 66-71 )