Integrated forecasting model based on LSTM and TCN for short-term power load forecasting

Abstract

Power load forecasting is important to ensure the stability and reliability of regional power systems. Researchers have put forward many combined forecasting models, but most of them cannot capture the global characteristics of data well. So as to improve the accuracy of short-term power load forecasting, this paper puts forward a combined forecasting model based on long-term and short-term memory networks (LSTM) and time convolution networks (TCN). In terms of the power load data, the LSTM and TCN forecasting models are established at first, and then the output results of LSTM and TCN are weighted and combined according to the reciprocal ratio of the square error, and the LSTM-TCN combined forecasting model is obtained. Finally, an example is analyzed by using the real data of the Australian Energy Administration. The LSTM-TCN model constructed in this paper has more advanced model performance, and its error is obviously lower than that of a single forecasting model and other classical network models, indicating that the LSTM-TCN model has higher accuracy in short-term load forecasting.