Since the existing monthly electricity consumption forecast has fewer influencing factors, and it is unable to comprehensively reflect the factors associated with strong electricity consumption. An elastic network electricity consumption forecasting model for high-dimensional data variable screening and high-precision prediction is proposed. The volume prediction model analyzes the monthly data of 340 variables and 96 time points for electricity consumption, economy, transportation, and meteorology. By using elastic network to screening for high-dimensional variables, and Granger causality analysis to find out the dependence of electricity consumption data and other data, the monthly electricity consumption of the whole society in a year is predicted. And the mean absolute percentage error of the prediction results is 3.07%. Compared with the VAR model, BP model and Lasso, the feasibility and effectiveness of the method are verified.