Energy Consumption Data Prediction Analysis based on EEMD-ARMA Model

Energy consumption data prediction model based on EEMD-ARMA prediction model is used to predict the nonlinear and non-stationary characteristics of industrial energy consumption data, which leads to the difficulty of data prediction and low prediction accuracy. This paper improves empirical mode decomposition (empirical mode decomposition, EMD) by ensemble empirical mode decomposition (ensemble empirical mode decomposition, EEMD) and predicts energy consumption by combining auto regressive sliding average model (auto regressive and moving average model, ARMA). Finally, the historical data of a certain system is selected as a sample, and the comparative analysis of EMD-ARMA and EEMD-ARMA prediction results is used to verify that the EEMD-ARMA algorithm used is more accurate than the prediction algorithm, and its prediction results have certain reference value for the control of energy consumption data in enterprises.