Forecasting of energy consumption by G20 countries using an adjacent accumulation grey model

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.

Predicting global energy demand for the next decade: A time-series model using nonlinear autoregressive neural networks

“…Raheem et al (2022) have developed an adjacent accumulation gray model for energy consumption forecasting of G20 countries up to 2026. From data processing to parameter estimation and analyzing the results, the model has shown promising model performance.…”

“…However, as the period is much longer, the uncertainty in load and source sides becomes a heavy factor in the model performance, which may need more extensive statistical and cross-validations. Raheem et al (2022) have developed an adjacent accumulation gray model for energy consumption forecasting of G20 countries up to 2026. From data processing to parameter estimation and analyzing the results, the model has shown promising model performance.…”

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

“…Economic growth will benefit from a shift in energy consumption from non-renewable to renewable [1,15]. The G20 countries, particularly the United States, Brazil, Canada, China, Germany, India, and the United Kingdom, are working to develop renewable energy and accelerate the adjustment of energy structures to be more environmentally friendly [5,16].…”

“…This study proxied energy demand with two control variables, unlike previous research. This control variable was chosen because population growth and urbanization are linked, and nations need a lot of energy for urban development (represented by the urban population) and rapid industrialization to meet financial goals [16,23].…”

Forecasting China's natural gas external dependence under the “Dual Carbon” goals by a new grey model

“…Other scholars also established model DAGM(1,1) for more big sample data with adjacent accumulation to forecast the natural gas consumption of G20 countries. 22 Because of the uncertainty of statistical data, some scholars established model MGM(1,1,⊗ b ) 23 for prediction. Considering the seasonal and periodic oscillation of natural gas demand and consumption, scholars discussed and established multiple models for prediction.…”