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Prediction and analysis of natural gas consumption in chongqing with a grey prediction model group in the context of COVID-19.
Zeng, Bo; Yang, Shuangyi; Mao, Cuiwei; Zhang, Dehai.
  • Zeng B; School of Management Science and Engineering Chongqing Technology and Business University Chongqing China.
  • Yang S; School of Management Science and Engineering Chongqing Technology and Business University Chongqing China.
  • Mao C; College of Wealth Management Chongqing Finance and Economics College Chongqing China.
  • Zhang D; School of Management Science and Engineering Chongqing Technology and Business University Chongqing China.
Energy Sci Eng ; 10(8): 2741-2755, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1813507
ABSTRACT
In this paper, a grey prediction model group is employed to quantitatively study the impact of COVID-19 on natural gas consumption in Chongqing, China. First, a grey prediction model group suitable for the prediction of Chongqing's natural gas consumption is introduced, which consists of GM(1,1), TWGM(1,1), and the newly-developed ODGM(1,1). Then, the model group is constructed to predict Chongqing's natural gas consumption in 2020. Finally, compare the predicted results of the model group with the actual consumption and quantitatively analyze the impact of the epidemic on natural gas in Chongqing. It is found that the impact of the epidemic on the consumption of natural gas in the first quarter of the year is very small, but relatively bigger in the second and third quarters. The study is of positive significance to maintain the supply and demand balance of natural gas consumption in Chongqing in the background of COVID-19; and it enriches and develops the theoretical system of grey prediction models.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Energy Sci Eng Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Energy Sci Eng Year: 2022 Document Type: Article