Daily Electricity Sales Forecasting by Industries Based on Time Convolution Network and Graph Attention Network

In order to control the power cost and improve the performance appraisal ability of the power department, it is necessary to make efficient and accurate forecast for the daily electricity sales. The deep learning Convolutional Neural Network (CNN) is often used in the power data prediction. However, the existing model has an upper limit for prediction, which makes it difficult to capture the long-term characteristics due to the limited information of its input data. For predicting the daily electricity sales efficiently and accurately, a prediction for the daily electricity sales of different industries based on the combination of the Time Convolution Network (TCN) and the Graph Attention Network (GAT) is proposed, and a high-dimensional prediction model for different industries is established. This method inputs the daily electricity sales of multiple industries at the same time, extracts the high-dimensional variables reflecting the timing characteristics of a single industry, and combines the high-dimensional variables of multiple industries to learn the influencing factors among the industries. The information of input data is increased through the integration of the daily electricity sales for multiple industries, so as to realize the forecast of the daily electricity sales for various industries. Taking the daily electricity sales of 21 industries in a city in Southeast China as an example, the average error of this method is 4.03%. Compared with the time convolution network (TCN), the Gated Recurrent Unit (GRU), the Facebook's Prophet model and the M4 champion ESRNN model, the results show that the forecast model proposed in this paper has higher prediction accuracy. © 2022, Power System Technology Press. All right reserved.