| The expansion of power grid scale and the improvement of power system automation level provide a data foun-dation and practical basis for accurate power consumption forecast based on big data technology. Aiming at the prominent problems such as high-dimensional data variable screening and high-precision prediction, this paper proposes an elastic network electricity consumption prediction model, which uses 340 variables for power con-sumption, economy, transportation and meteorology, and monthly data of 96 time points. Perform correlation analysis. Elastic network factor screening for high-dimensional variables, and Granger causality analysis to find out the dependence of electricity consumption data and other data, predicting the monthly electricity consumption of the whole society in a year, and the average absolute value of the prediction results. The percentage error is con-trolled within 2%, and a good prediction effect is obtained. Compared with VAR model, BP model and Lasso, the prediction results show that the method selected in this paper has high precision, which verifies the feasibility and effectiveness of the method.