【作者】 陈涛；

【导师】 解建光；

【作者基本信息】 南京航空航天大学 ， 工程硕士（专业学位）， 2019， 硕士

【摘要】 近年来,保护环境和节能减排逐渐成为备受关注的热点问题,作为能源消耗的重点行业,交通运输业具有能耗源头多、规模大等特点,我国各级政府也大力开展了绿色交通工作,逐步地把能耗核查纳入到政府的重点工作之中。传统能耗统计方法从能源口径出发,未能考虑到运输工具终端的实际能耗情况,能耗数据普遍存在失真的问题,基于这一现状,论文针对区域交通运输能耗统计监测和预测模型展开了研究。论文以辽宁省为例,提出了区域水陆交通能耗统计和监测方案,经过实地调研,制定了能耗统计表格,确定了能耗统计监测的方法及频率,选取了5888个统计样本进行统计分析,并对其中339个样本进行了实时监测。统计和监测结果表明,在能耗差值方面,城市公交和水路货运领域的监测单耗数据较现有统计数据分别低出8.43%和10.87%,公路货运和公路客运领域的监测单耗数据较现有统计数据分别高出5.93%和4.43%,总能耗的监测数据较现有统计数据高出1.97%,应对能耗统计数据进行修正。此外,论文基于常住人口数量、人均GDP、城市化率、汽车保有量、水路船舶净载重量、交通运输周转量和能源利用效率系数等7个能耗影响因素,采用STIRPAT模型和岭回归分析方法,建立了区域交通运输能耗预测模型,得到7项指标的敏感性系数分别为1.213、0.036、0.535、0.065、0.075、0.044和-0.036。模型的验证结果表明,预测模型的最大误差为5.8%,平均误差仅为2.59%,说明模型具备良好的精度,同时,论文采用修正系数对预测模型进行了修正,得到基于监测数据的区域交通运输能耗预测模型,相较于基于现有统计数据的预测模型,修正后的模型更能反映交通运输业的真实能耗。最后,论文采用7项能耗影响因素在2018-2030年的拟合数据对辽宁省交通运输业能耗情况进行了预测,结果表明,能耗总量呈先增后减趋势,并在2020年达到峰值,主要原因在于人口变化及能源利用效率的提高。总体而言,论文通过对现有能耗统计方法和实际监测结果的分析,建立了基于统计数据的区域交通运输能耗预测模型,提出了修正系数对能耗预测模型进行修正,得到了基于监测数据的能耗预测模型,并对2018-2030年辽宁省交通运输业能耗总量进行了预测,研究成果可为其他地区交通运输业的能耗统计和预测提供指导和参考。更多还原

【Abstract】 In recent years,environmental protection,energy conservation and emission reduction have gradually become a hot issue of concern.As a key industry of energy consumption,transportation industry has many characteristics such as energy sources and large scale.Chinese governments have also vigorously carried out green transportation work,and gradually put energy consumption verification into the key works of government.The traditional energy consumption statistical methods start from the energy caliber,does not take the actual consumption of transport vehicles into account,and there is a widespread distortion of energy consumption data.Based on this situation,this paper studies the statistical monitoring and prediction model of regional transportation energy consumption.Liaoning Province was taken as an example to carry out the research.The regional water and land transportation energy consumption statistical and monitoring scheme was put forward in this paper.After field investigation,the energy consumption statistics table was drawn up,the method and frequency of energy consumption statistical monitoring were determined,5888 statistical samples are selected for statistical analysis,and 339 of them are monitored in real time.Statistical and monitoring results show that the monitoring data in the field of bus and freighter are 8.43% and 10.87% lower than the statistical data,and the monitoring data in the field of truck and coach are 5.93% and 4.43% higher than the statistical data.Further analysis shows that the monitoring data of total energy consumption is 1.97% higher than the statistical data,and the statistical data of energy consumption should be revised.In addition,a prediction model of regional transportation energy consumption has been put forward based on the seven factors affecting energy consumption,such as permanent resident population,per capita GDP,urbanization rate,car ownership,net load weight of ship,transportation turnover and energy utilization efficiency coefficient,and the sensitivity coefficients of these seven indicators were proposed as 1.213,0.036,0.535,0.065,0.075,0.044 and-0.036,respectively.Verification results of the prediction model show that the maximum error of the prediction model is 5.8%,and the average error is only 2.59%,which means the model is highly correlated with datas.Besises,a correction coefficient was proposed to modify the prediction model,and the regional transportation energy consumption prediction model based on monitoring data were obtained,compared with the original model,the revised model can reflect the real energy consumption of transportation industry.Finally,the fitting data of seven energy consumption factors from 2018 to 2030 was carried to forecast the energy consumption of transportation industry in Liaoning Province.The results show that the total energy consumption increases first and then decreases,and reaches its peak in 2020,the main reason is the population change and the improvement of energy utilization efficiency.Overall,through the analysis of the existing energy consumption statistical methods and actual monitoring results,a regional transportation energy consumption prediction model based on statistical data was established,a correction coefficient was proposed to modify the energy consumption prediction model,which is helpful to obtain the energy consumption prediction model based on monitoring data.The total energy consumption of Liaoning transportation industry in 2018-2030 has been forecasted as well.The research findings can provide guidance and reference for energy consumption statistics and prediction of transportation industry in other areas.更多还原