Abstract
Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.
Highlights
Under the circumstance of Chinese energy planning program of “13th Five-Year Plan”, the energy revolution was proposed to vigorously promote clean and low carbon energy supply
This study constructed the optimized extreme learning machine forecasting model based on grey prediction theory and support vector machine, which can improve the accuracy of the forecasting result of carbon emissions
The main structure of this article is as follows: Section 2 summarizes the principles of support vector machine algorithm, grey prediction theory and the optimized extreme learning machine algorithm, which are the theoretical basis of the study
Summary
Under the circumstance of Chinese energy planning program of “13th Five-Year Plan”, the energy revolution was proposed to vigorously promote clean and low carbon energy supply. This study constructed the optimized extreme learning machine forecasting model based on grey prediction theory and support vector machine, which can improve the accuracy of the forecasting result of carbon emissions. The optimized SVM-ELM forecasting model was used to achieve an accurate prediction of the amount of carbon emissions related to energy consumption in the Beijing-Tianjin-Hebei region from 2017 to 2030. A new forecasting model based on the extreme learning machine algorithm optimized by grey prediction theory and support vector machine is proposed. The main structure of this article is as follows: Section 2 summarizes the principles of support vector machine algorithm, grey prediction theory and the optimized extreme learning machine algorithm, which are the theoretical basis of the study.
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