Forecasting and Analysis of Energy Consumption in China: Based on Grey Forecasting Model

Lei Gao

doi:10.54097/fbem.v3i2.257

Authors

Lei Gao

DOI:

https://doi.org/10.54097/fbem.v3i2.257

Keywords:

Total energy consumption, GM(1,1), DGM(1,1), Grey Verhulst model.

Abstract

Energy is essential to the development of an economy and society. In recent years, China's rapid economic development has created the "China Miracle", but it has also led to a sharp increase in energy consumption in China. To ensure the achievement of the ambitious goal of reaching the carbon peak by 2030, it is of great significance to study the total energy consumption in China in order to promote the national energy conservation and emission reduction actions. This paper constructs models GM(1,1), DGM(1,1), and gray Verhulst model based on the original data of China's total energy consumption from 2001 to 2020, and constructs a combined forecasting model by the least squares method to make an economic forecast of China's energy consumption in the next five years. It provides a theoretical basis for making a reasonable energy planning.

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References

Claudia F, Cinzia M. Forecasting the impact of renewable energies in competition with non-renewable sources [J]. Renewable and Sustainable Energy Reviews, 2018, 81(2): 1879-1886. DOI: https://doi.org/10.1016/j.rser.2017.05.284

Xia C X, Wang Z L. Drivers analysis and empirical mode decomposition based forecasting of energy consumption structure [J]. Journal of Cleaner Production, 2020, 254: 120107. DOI: https://doi.org/10.1016/j.jclepro.2020.120107

Jana R K, Ghosh I, Sanyal M K. A granular deep learning approach for predicting energy consumption [J]. Applied Soft Computing, 2020, 89: 106091. DOI: https://doi.org/10.1016/j.asoc.2020.106091

Yu L, Zhao Y Q, Tang L, et al. Online big data-driven oil consumption forecasting with Google trends [J]. International Journal of Forecasting, 2019, 35(1): 213-223. DOI: https://doi.org/10.1016/j.ijforecast.2017.11.005

Qiao W B, Yang Z, Kang Z Y, et al. Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm [J]. Engineering Applications of Artificial Intelligence, 2020, 87: 103323. DOI: https://doi.org/10.1016/j.engappai.2019.103323

Zhou Dadi, Dai Yande, Yu Cong et al. 2020 Sustainable energy scenarios for China [M]. Beijing: China Environmental Science Press, 2003:568-627.

Wan Lei, Zheng Jiliang. Energy consumption analysis and scenario prediction in Yunnan Province based on IPAT equation[J]. Journal of Kunming University of Science and Technology (Natural Science Edition), 2009, 34(5):93-96.

Pang Jiaxing, Chen Xingpeng, Wang Huiyu. Study on the relationship between energy consumption and economic growth and energy consumption forecast in Gansu Province [J]. Arid Zone Resources and Environment, 2014, 28(2):31-36.

Zhu YN, Li LF, He S, Li H, Wang Y. Carbon emission peak year projection in Shanxi Province based on IPAT model and scenario analysis method [J]. Resource Science, 2016,38(12):2316-2325. DOI: https://doi.org/10.18402/resci.2016.12.11

Nie R, Zhang T, Wang D. Study of energy consumption and carbon emission scenarios in Jiangsu Province based on IPAT model [[J]. Journal of Natural Resources, 2010,25 (09) :15 57-15 64.

Zhu Lujie, Hou Xiangzhao. Analysis of medium and long-term residential building electric energy consumption scenarios in Quanzhou [[J]. Fujian Architecture, 2011(12):44-46.

Huang F, Jiang KS, Lu WQ, Wu Y. Analysis of energy demand and carbon emission scenarios in Shanghai under low carbon economy [[J]. East China Economic Management, 2012,26(04):1-4.

Gao S-L, Ren Z-J. Scenario prediction of energy consumption and environmental emissions of urban passenger transportation in Beijing[J]. Journal of Automotive Safety and Energy Conservation, 2015,6(03):259-264.

Zhu X., Cao J. Research on the prediction of energy consumption demand in Jiangsu manufacturing industry based on GM}1,1} model [[J]. Journal of Reading River, 2010,2(02):39-46.

Gao H, Xu Pepeng. Electricity consumption forecasting in China's manufacturing industry-Example of GM(1,1) model[[J]. Management Observation Observation, 2014(07):42-44.

Yang Haochang, Wu Cui, Wang Jiawei, Yu Caihua. Prediction of manufacturing development level in Jiangsu and analysis of influencing factors-Based on grey system theory[J]. Journal of Reading River, 2015,7(02):45-51.

Zhou Caihong, Fan Lijun. International comparison and prediction of new manufacturing degree based on first power [J]. China Science and Technology Forum, 2016(11):141 a 147+154.

Cheng W R, Yao T C. Prediction of energy consumption of chemical raw materials and chemical products manufacturing industry in Nanjing [J]. Industrial Safety and Environmental Protection 2016,42}o1}a9-92+99.

Gong ZJ. Prediction of major energy consumption for manufacturing development in China based on GM}1,1} model [J]. Science and Technology Innovation, 2018(03):155 I160.