Prediction of Surface Temperature and CO2 Emission using a novel grey system model

The increase in surface temperature and CO2 emissions are two of the most important issues in climate studies and global warming. The ‘Global Emissions 2021’ report identifies the six biggest contributors to CO2­ emissions; China, USA, India, Russia, Japan, and Germany. The current study projects the increase in surface temperature and the CO­2 emissions of these six countries by 2028. The EGM (1,1,α,θ) grey model is an even form of the model with a first order differential equation, that has one variable and a weightage background value that contains conformable fractional accumulation. The results show that while the CO2 emissions for Japan, Germany, USA and Russia show a downward projection, they are expected to increase in India and remain nearly constant in China by 2028. The surface temperature has been projected to increase at a significant rate in all these countries. By comparing with the EGM (1,1) grey model, the results show that the EGM (1,1, α, θ) model performs better in both in-sample and out-of-sample forecasting. The paper also puts forward some policy suggestions to mitigate, manage and reduce increases in surface temperature as well as CO2 emissions.

Forecasting Natural Gas Consumption in the US Power Sector by a Randomly Optimized Fractional Grey System Model

Natural gas is one of the main energy resources for electricity generation. Reliable forecasting is vital to make sensible policies. A randomly optimized fractional grey system model is developed in this work to forecast the natural gas consumption in the power sector of the United States. The nonhomogeneous grey model with fractional-order accumulation is introduced along with discussions between other existing grey models. A random search optimization scheme is then introduced to optimize the nonlinear parameter of the grey model. And the complete forecasting scheme is built based on the rolling mechanism. The case study is executed based on the updated data set of natural gas consumption of the power sector in the United States. The comparison of results is analyzed from different step sizes, different grey system models, and benchmark models. They all show that the proposed method has significant advantages over the other existing methods, which indicates the proposed method has high potential in short-term forecasting for natural gas consumption of the power sector in United States.

Correction to: Improvement of Grey System Model using Particle Swarm Optimization

Following publication of the original article [1], the authors reported an error in the title and body text.

Application of Grey System Model to Forecast the Natural Gas Imports in China

China’s natural gas imports will keep an upward trend in the future due to its increasing demands. A comparatively accurate prediction of natural gas imports will help the Chinese government make appropriate decisions when formulating energy policies. In this paper, a new grey predication model, GPM_NGI model, was proposed to forecast China’s natural gas imports. Compared with GM (1, 1) and DGM (1, 1) model, the proposed new model performed better in the simulation process and bore smaller mean relative percentage error when used in simulating China’s natural gas imports from 2011 to 2019. Then, the new model was employed to forecast China’s natural gas imports from 2020 to 2022. The results showed that China’s natural gas imports would continue to grow rapidly over the next three years. Therefore, in order to strike a balance between the natural gas supply and demand in the future and avoid overdependence on imports, the Chinese government should take effective measures from both the supply and demand ends, such as making better use of shale gas, wind, and solar energy as well as reducing the industrial dependence on natural gas.