Comparative Analysis of Load Forecasting Based on LSTM and ARIMA

Abstract

To address the issue of improving the accuracy of load forecasting, research on load forecasting based on different principles was conducted. First, data preprocessing was performed. Secondly, a load forecasting process based on LSTM was formed, followed by a load forecasting process based on ARIMA. Finally, a case analysis was conducted, which verified that the LSTM-based prediction method has higher accuracy in predicting the comprehensive load in the region. Furthermore, the advantages and disadvantages of the two methods were analyzed. The ARIMA model has a simple calculation, but it is difficult to accurately predict complex nonlinear load changes. On the other hand, the LSTM model can better handle complex nonlinear load changes, but it requires high