LAUSR

The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.