Research on the short-term electric load forecasting model based on the graph learning layer and timeseries convolutional network

Abstract

Aiming at the problem of a complex relationship and easy loss of time series length information in power load forecasting tasks. In this paper, a short-term power load forecasting model based on GLL-TCN is constructed. Firstly, the graph learning layer is taken to extract the unidirectional relationships between variables to form an adjacency matrix to obtain the asymmetric features of variables. Secondly, the graph convolutional network is improved, and the signals of each node are fused and propagated through the graph convolutional network, so that the characteristics of each node contain the load data information of its neighbor nodes. Finally, in the temporal convolutional network, the inflated convolutional module layer is used to process the load data of longer series, expand the receptive field, and generate a prediction model. Experiments are carried out on the power dataset and compared with GP, RNN-GRU, AR, LSTNet and TPA-LSTM models, the experimental results indicate that the accuracy of prediction of the GLL-TCN model is better than the base line model at different time intervals, thus proving the accuracy of the model in load forecasting.