Can Machine Learning be Applied to Carbon Emissions Analysis: An Application to the CO2 Emissions Analysis Using Gaussian Process Regression
Ning Ma, Wai Yan Shum, Tingting Han, Fujun Lai
Abstract
In this paper, a nonparametric kernel prediction algorithm in machine learning is applied to predict CO 2 emissions. A literature review has been conducted so that proper independent variables can be identified. Traditional parametric modeling approaches and the Gaussian Process Regression (GPR) algorithms were introduced, and their prediction performance was summarized. The reliability and efficiency of the proposed algorithms were then demonstrated through the comparison of the actual and the predicted results. The results showed that the GPR method can give the most accurate predictions on CO 2 emissions.
Topics & Concepts
KrigingGaussian processReliability (semiconductor)Ground-penetrating radarKernel (algebra)Nonparametric statisticsProcess (computing)Parametric statisticsRegressionMachine learningRegression analysisComputer scienceNonparametric regressionGaussianArtificial intelligenceAlgorithmStatisticsMathematicsChemistryRadarOperating systemComputational chemistryQuantum mechanicsPhysicsTelecommunicationsCombinatoricsPower (physics)Environmental Impact and SustainabilityEnergy, Environment, and Transportation PoliciesAir Quality Monitoring and Forecasting