Under the background of the novel power system

the electricity consumption behavior of various industries presents complex dynamic characteristics. Accurate regional monthly electricity prediction is of great significance for the power sector to carry out grid planning and operation scheduling. To accurately grasp the impact relationship of electricity consumption in various industries and improve prediction accuracy

a new monthly electricity prediction method for power systems based on multi-scale time-series graph feature mining of segmented industries is proposed. A multi-industry temporal feature independent encoding multi-temporal-attention parallel network and multi-scale convolutional network are established to achieve deep characterization of the electricity consumption characteristics of various segmented industries in different cycles such as weeks

months

and quarters. A graph convolutional neural network for multi-industry user temporal coupling mining is proposed

which establishes the mutual influence relationship between different industries through graph models

and implements regional electricity consumption prediction through convolutional network decoding. The analysis results based on the historical operation data of the Beijing-Tianjin-Tangshan power grid from 2020 to 2022 demonstrate that the prediction method in this paper can reduce the RMSE error by an average of 658 million kWh and the MAPE error by an average of 1.53%. The experiment has verified the effectiveness

accuracy

and feasibility of the monthly electricity forecasting method proposed in this paper.