Research on Carbon Emission Prediction of Energy Consumption Based on Neural Network and Scenario Analysis
Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China
Tagungsband: ISCTT 2021
Seiten: 6Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Zhang, Wei; Zhang, Zhizhi; Xu, Zhenan; Li, Fang; Liu, Zesan; Meng, Hongmin; Yan, Yurong (State Grid Information and Telecommunication Group Co., Ltd. Changping District, Beijing, China)
Zhang, Xiao (Communication University of China, Nanjing, Jiangning District, Nanjing City, Jiangsu Province, China)
Based on the yearbook data of Jiangsu Province from 2000 to 2019, such as population, GDP index, energy intensity, energy structure, foreign investment level, urbanization level and industrial structure, 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.