CARBON EMISSION PREDICTION OF TRANSPORTATION INDUSTRY BASED ON VMD AND SSA-LSSVM

WANG Qingrong; WANG Junjie; ZHU Changfeng; HAO Fule

doi:10.13205/j.hjgc.202310016

Volume 41 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(10): 124-132

WANG Qingrong, WANG Junjie, ZHU Changfeng, HAO Fule. CARBON EMISSION PREDICTION OF TRANSPORTATION INDUSTRY BASED ON VMD AND SSA-LSSVM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 124-132. doi: 10.13205/j.hjgc.202310016

Citation:

WANG Qingrong, WANG Junjie, ZHU Changfeng, HAO Fule. CARBON EMISSION PREDICTION OF TRANSPORTATION INDUSTRY BASED ON VMD AND SSA-LSSVM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(10): 124-132. doi: 10.13205/j.hjgc.202310016

Citation:

PDF( 2155 KB)

CARBON EMISSION PREDICTION OF TRANSPORTATION INDUSTRY BASED ON VMD AND SSA-LSSVM

doi: 10.13205/j.hjgc.202310016

1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. School of Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Received Date: 2023-05-18
Available Online: 2023-12-26

Abstract

Abstract

Aiming at the volatility and nonlinearity of transport carbon emission data series, a combined prediction model combining variational mode decomposition (VMD), sparrow search algorithm (SSA), and least square support vector machine (LSSVM) was adopted to predict transport carbon emission more accurately. Firstly, the VMD method was used to decompose the original carbon emission data series into multiple low-complexity, stable modal components, and a residual term to reduce the volatility and nonlinearity of the carbon emission data series. Secondly, the LSSVM model was established for each decomposition module, and the parameters of the LSSVM model were optimized by SSA. Finally, the prediction results of each module were integrated and superimposed to obtain the final carbon emission prediction results. The carbon emission data of China's transportation industry from 1990 to 2019 were calculated to verify the model and compare it with various models. The results showed that the root-mean-mean error, mean absolute error, mean absolute percentage error, determination coefficient and Nash coefficient of the VMD-SSA-LSSVM model was 6.28 million t, 5.74 million t, 0.73%, 0.998 and 0.996, respectively, which was superior to other models, indicating that the model can effectively improve the prediction accuracy.
- transportation industry,
- variational mode decomposition,
- sparrow search algorithm,
- least squares support vector machine,
- carbon emission prediction

FullText(HTML)

References(22)

References

[1]	LIN B, XIE C.Reduction potential of CO₂, emissions in China's transport industry[J].Renewable & Susyainable Energy Reviews, 2014(33):689-700.
[2]	张新生, 任明月, 陈章政.基于CEEMD-SSA-ELM方法的建筑业碳排放预测研究[J/OL].生态经济:1-15[2023-04-14 ].http://kns.cnki.net/kcms/detail/53.1193.F.20221214.1300.002.html.
[3]	YANG H J, O'CONNELL J F.Short-term carbon emissions forecast for aviation industry in Shanghai[J].Journal of Cleaner Production, 2020, 275:122734.
[4]	王阳, 唐朝晖, 王紫勋, 等.选用改进高斯过程回归模型的碳排放短期预测[J].计算机工程与应用, 2018(23):246-251.
[5]	QIAO W B, LU H F, ZHOU G F, et al.A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer[J].Journal of Cleaner Production, 2020, 244:118612.
[6]	GAO M Y, YANG H L, XIAO Q Z, et al.A novel method for carbon emission forecasting based on Gompertz's law and fractional grey model:evidence from American industrial sector[J].Renewable Energy, 2022, 181:803-819.
[7]	陈亮, 王金泓, 何涛, 等.基于SVR的区域交通碳排放预测研究[J].交通运输系统工程与信息, 2018, 18(2):13-19.
[8]	徐勇戈, 宋伟雪.基于FCS-SVM的建筑业碳排放预测研究[J].生态经济, 2019, 35(11):37-41.
[9]	王珂珂, 牛东晓, 甄皓, 等.基于WOA-ELM模型的中国碳排放预测研究[J].生态经济, 2020, 36(8):20-27.
[10]	张雯, 吴志彬, 徐玖平.基于EMD-PSO-LSSVM的碳排分解集成预测方法[J].控制与决策, 2022, 37(7):1837-1846.
[11]	BOKDE N D, TRANBERG B, ANDRESEN G B.Short-term CO₂ emissions forecasting based on decomposition approaches and its impact on electricity market scheduling[J].Applied Energy, 2021, 281:116061.
[12]	LI W, GAO S B.Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China's cement industry[J].Energy, 2018, 165:33-54.
[13]	IPCC.2006 IPCC Guidelines for National Greenhouse Gas Inventories[R].Cambridge:Cambridge University Press, 2006.
[14]	贾顺平, 毛保华, 刘爽, 等.中国交通运输能源消耗水平测算与分析[J].交通运输系统工程与信息, 2010, 10(1):22-27.
[15]	刘淳森, 曲建升, 葛钰洁, 等.基于LSTM模型的中国交通运输业碳排放预测[J].中国环境科学, 2023, 43(5):2574-2582.
[16]	国家统计局能源统计司, 国家能源局综合司.中国能源统计年鉴2021[M].北京:中国统计出版社, 2021.
[17]	DRAGOMIRETSKIY K, ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing, 2013, 62(3):531-544.
[18]	XU B Z, FENG X, LI H, et al.Early fault feature extraction of bearings based on Teager energy operator and optimal VMD[J].ISA transactions, 2018.
[19]	XUE J K, SHEN B.A novel swarm intelligence optimization approach:sparrow search algorithm[J].Systems Science & Control Engineering, 2020, 8(1):22-34.
[20]	池小波, 续泽晋, 贾新春, 等.基于WPD-ISSA-CA-CNN模型的电厂碳排放预测[J/OL].控制工程:1-8[2023-09-09 ].DOI: 10.14107/j.cnki.kzgc.20220983.
[21]	王瑞, 王超, 逯静.基于VMD-ICSA-LSSVM的短期光伏功率预测[J].制造业自动化, 2023, 45(3):124-129.
[22]	潘思羽, 张美玲.基于BP神经网络的甘肃省二氧化碳排放预测及影响因素研究[J].环境工程, 2023, 41(7):61-68 , 85.