2nd China International Youth Conference on Electrical Engineering (CIYCEE 2021)

230 / 2021-11-03 09:33:28

Research on Carbon Emission Prediction of Energy Consumption Based on Neural Network and Scenario Analysis

AI-driven technology > Other related AI-driven techniques

Based on the yearbook data of population, GDP index, energy intensity, energy structure, foreign investment level, urbanization level and industrial structure in Jiangsu Province from 2011 to 2019, an Elman neural network carbon emission prediction model was constructed. On this basis, the scenario analysis method was used to predict the peak time of carbon emission in Jiangsu Province under different scenarios, and the relationship between the total carbon emission and various influencing factors was analyzed. The research showed that there was a negative correlation between energy intensity index and carbon emission, and other influencing factors have a differential positive correlation for carbon emission in Jiangsu Province. The research results put forward some energy-saving and emission reduction countermeasures according to the prediction trend of carbon emission peak in Jiangsu under different scenarios.