Prediction of Industrial Power Consumption and Air Pollutant Emission in Energy Internet

Abstract

The energy internet integrated the information technology into the renewable energy can solve the energy shortage and environmental pollution problems. This paper studies the prediction of the power consumption in the energy internet based on the linear regression and random forest algorithms. Based on the predicted power consumption and the emission factors, the emission of the major air pollutants, i.e., PM, NOx and SO2, in the cement industry are predicted. Simulation results show that these two predicted algorithms can achieve the accuracy performance as much as 89.4% and 97.6%, respectively. It also demonstrates that the predicted amount of PM emission is much more than NOx and SO2