A Novel Fractional Accumulative Grey Multivariable Regression Model with GA Optimizer for Forecasting Short-Term CO2 Emissions in Malaysia

Global warming is mostly caused by carbon dioxide (CO2) emissions. The Malaysian government needs to analyze and forecast CO2 emissions in order to develop effective energy and environmental policies. The purpose of this research is to create a simple multivariate regression prediction approach for real-time CO2 emissions. The proposed prediction approach is based on a modified fractional grey multivariate regression model with a genetic algorithm optimizer, known as the FGML(0,N) model, for forecasting CO2 emissions. The proposed FGML(0,N) prediction accuracy was validated using a real-world CO2 emission case. Experimental results showed that the proposed FGM(0,N) performed well compared to the multivariable regression model (MLR) and the support vector regression (SVR) models.