Carbon emission combined prediction of main ports in China based on variational modal decomposition

CHEN Zhefeng; WANG Jian

doi:10.3969/j.issn.1000-4653.2024.04.018

Navigation of China > 2024 > 47(4): 138-145. > DOI: 10.3969/j.issn.1000-4653.2024.04.018

CHEN Zhefeng, WANG Jian. Carbon emission combined prediction of main ports in China based on variational modal decomposition[J]. Navigation of China, 2024, 47(4): 138-145. DOI: 10.3969/j.issn.1000-4653.2024.04.018

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Carbon emission combined prediction of main ports in China based on variational modal decomposition

CHEN Zhefeng,
WANG Jian^,

School of Economics and Management, Fuzhou University, Fuzhou 350108, China

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Received Date: June 20, 2023

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Abstract

As a major energy-consuming and greenhouse gas emitter,the study of carbon emission trends in ports is crucial for promoting the construction of green and ecological ports in China.Considering the multi-scale characteristics of port carbon emission fluctuations,this paper takes the major container ports in China as the objects,and constructs a multi-scale combined forecasting model integrating Variational Modal Decomposition-Wavelet Neural Network-Genetic Algorithm-Back Propagation Neural Network（VMD-WNN-GA-BPNN）.Based on the idea of decomposition-subsequence forecastingensemble forecasting,the carbon emissions series are decomposed into multiple modal components by using VMD.The components are classified into low,medium and high frequency and trend terms according to their fluctuation characteristics,then,this paper optimizes forecast method respectively to achieve itemized forecast,and completes an integrated forecast and analyzes the effect of the forecast using the sub-predicted values.Example applications show that,compared with the existing prediction models,the multi-scale combined prediction model constructed in the paper can significantly improve the prediction accuracy of port carbon emissions and reveal the intrinsic multi-scale characteristics of port carbon emissions,which is conducive to the formulation of targeted carbon emission reduction strategies from the scales of energy technologies,seasons,and emergencies.
- container port,
- carbon emission prediction,
- variational modal decomposition,
- multi-scale combined forecasting model

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[1]	MISRA A,PANCHABIKESAN K,GOWRISHANKAR S K,et al.GHG emission accounting and mitigation strategies to reduce the carbon footprint in conventional port activities-a case of the Port of Chennai[J].Carbon Management,2017,8(1):45-56.
[2]	FARHAN J,ONG G P.Forecasting seasonal container throughput at international ports using SARIMA models[J].Maritime Economics&Logistics,2018,20(1):131-148.
[3]	肖进,文章,刘博,等.基于选择性深度集成的集装箱吞吐量混合预测模型研究[J].系统工程理论与实践,2022,42(4):1107-1128.XIAO J,WEN Z,LIU B,et al.A hybrid model based on selective deep-ensemble for container throughput forecasting[J].Systems Engineering-Theory&Practice,2022,42(4):1107-1128.(in Chinese)
[4]	XU X F,BAI X N,XIAO Y J,et al.A port ship flow prediction model based on the automatic identification system and gated recurrent units[J].Journal of Marine Science and Application,2021,20(3):572-580.
[5]	吴涵,张立,刘岱,等.港口物流需求趋势预测方法研究--基于组合预测模型对重庆港口物流需求趋势分析[J].价格理论与实践,2019(9):75-78.WU H,ZHANG L,LIU D,et al.Research on forecasting method of port logistics demand trendanalysis of Chongqing port logistics demand trend based on combined forecasting model[J].Price:Theory&Practice,2019(9):75-78.(in Chinese)
[6]	纪建悦,孔胶胶.基于STIRFDT模型的海洋交通运输业碳排放预测研究[J].科技管理研究,2012,32(6):79-81.JI J Y,KONG J J.Prediction research on carbon emissions of marine transportation based on STIRFDT model[J].Science and Technology Management Research,2012,32(6):79-81.(in Chinese)
[7]	门联欢,甘爱平,陈可桢.绿色港口趋势下我国主要港口碳排放量的预测及减排对策[J].水运管理,2014,36(8):6-8.MEN L H,GAN A P,CHEN K Z.Prediction of carbon emissions from major ports in China under the trend of green ports and measures to reduce emissions[J].Shipping Management,2014,36(8):6-8.(in Chinese)
[8]	赵瑞嘉,谢燕,宿博涵,等.集装箱港口碳排放量及碳达峰时间测算方法[J].大连海事大学学报,2021,47(4):56-64.ZHAO R J,XIE Y,SU B H,et al.Carbon emissionand carbon peak time calculation method of container port[J].Journal of Dalian Maritime University,2021,47(4):56-64.(in Chinese)
[9]	YU Y,SUN R K,SUN Y D,et al.Integrated carbon emission estimation method and energy conservation analysis:the port of Los Angles case study[J].Journal of Marine Science and Engineering,2022,10(6):717.
[10]	张国兴,苏钊贤.黄河流域交通运输碳排放的影响因素分解与情景预测[J].管理评论,2020,32(12):283-294.ZHANG G X,SU Z X.Analysis of influencing factors and scenario prediction of transportation carbon emissions in the Yellow River basin[J].Management Review,2020,32(12):283-294.(in Chinese)
[11]	LIU M Z,ZHANG X X,ZHANG M Y,et al.Influencing factors of carbon emissions in transportation industry based on CD function and LMDI decomposition model China as an example[J].Environmental Impact Assessment Review,2021,90:106623.
[12]	张国兴,张振华,刘鹏,等.我国碳排放增长率的运行机理及预测[J].中国管理科学,2015,23(12):86-93.ZHANG G X,ZHANG Z H,LIU P,et al.The running mechanism and prediction of the growth rate of China's carbon emissions[J].Chinese Journal of Management Science,2015,23(12):86-93.(in Chinese)
[13]	喻洁,达亚彬,欧阳斌.基于LMDI分解方法的中国交通运输行业碳排放变化分析[J].中国公路学报,2015,28(10):112-119.YU J,DA Y B,OUYANG B.Analysis of carbon emission changes in China's transportation industry based on LMDI decomposition method[J].China Journal of Highway and Transport,2015,28(10):112-119.(in Chinese)
[14]	DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE transactions on signal processing,2013,62(3):531-544.
[15]	LI J L,SONG Z H,WANG X F,et al.A novel offshore wind farm typhoon wind speed prediction model based on PSO-Bi-LSTM improved by UMD[J].Energy,2022,251:123848.
[16]	LIU H Y,HAN H,SUN Y,et al.Short-term wind power interval prediction method using VMD-RFG and ATT-GRU[J].Energy,2022,251:123807.
[17]	LAHMIRI S.A variational mode decomposition approach for analysis and forecasting of economic and financial time series[J].Expert Systems with Applications,2016,55:268-273.
[18]	JIN Z Z,HE D Q,WEI Z X.Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN[J].Engineering Applications of Artificial Intelligence,2022,110:104713.
[19]	WANG Z F,HE X Q,SHEN H L,et al.Multi-source information fusion to identify water supply pipe leakage based on SVM and VMD[J].Information Processing and Management,2022,59(2):102819.
[20]	胡邦辉,成龙,王学忠,等.长江中下游汛期降水GABP预测模型[J].解放军理工大学学报(自然科学版),2016,17(6):564-570.HU B H,CHENG L,WANG X Z,et al.GA-BP model for forecasting rain season precipitation over middlelower reaches of Yangtze River[J].Journal of PLA University of Science and Technology (Natural Science Edition),2016,17(6):564-570.(in Chinese)
[21]	潘玉民,赵立永,张全柱.高频随机时间序列分量的稳定预测方法[J].计算机工程与设计,2013,34(6):2262-2266.PAN Y M,ZHAO L Y,ZHANG Q Z.Stable prediction method of high frequency random time series component[J].Computer Engineering and Design,2013,34(6):2262-2266.(in Chinese)
[22]	郭瑾,匡海波,余方平.基于Gamma的低碳港口形成机理研究--以日照港为例[J].科研管理,2020,41(5):240-249.GUO J,KUANG H B,YU F P.A research on the port low carbon evolution mechanism based on Gamma function:a case study of Rizhao Port[J].Science Research Management,2020,41(5):240-249.(in Chinese)

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