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Abstract
This paper studies an adjacent accumulation discrete grey model to improve the prediction of the grey model and enhance the utilization of new data. The impact of COVID-19 on the global economy is also discussed. Two cases are discussed to prove the stability of the adjacent accumulation discrete grey model, which helped the studied model attain higher forecasting accuracy. Using the adjacent accumulation discrete grey model, non-renewable energy consumption in G20 countries from 2022 to 2026 is predicted based on their consumption data from 2011 to 2021. It is proven that the adjacent accumulation exhibits sufficient accuracy and precision. Forecasting results obtained in this paper show that energy consumption of all the non-renewable sources other than coal has an increasing trend during the forecasting period, with the USA, Russia, and China being the biggest consumers. Natural gas is the most consumed non-renewable energy source between 2022 and 2026, whereas hydroelectricity is the least consumed. The USA is the biggest consumer of Nuclear energy among the G20 countries, whereas Argentina consumed only 0.1 Exajoules of nuclear energy, placing it at the end of nuclear energy consumers.
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Details
1 Curtin University, Department of Chemical Engineering, Faculty of Engineering and Science, Miri Sarawak, Malaysia (GRID:grid.448987.e)
2 Universiti Teknologi Brunei, Petroleum and Chemical Engineering, Faculty of Engineering, Bandar Seri Begawan, Brunei Darussalam (GRID:grid.454314.3)
3 Jaypee Institute of Information Technology, Department of Physics and Materials Science and Engineering, Noida, India (GRID:grid.419639.0) (ISNI:0000 0004 1772 7740)
4 King Saud University, Department of Biochemistry, College of Science, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396)
5 Dawood University of Engineering and Technology, Department of Chemical Engineering, Karachi, Pakistan (GRID:grid.449033.9) (ISNI:0000 0004 4680 6835)
6 RMIT University, School of Engineering, Melbourne, Australia (GRID:grid.1017.7) (ISNI:0000 0001 2163 3550)