资源科学 ›› 2016, Vol. 38 ›› Issue (3): 450-460.doi: 10.18402/resci.2016.03.08

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基于RBF神经网络的物流业能源需求预测

李瑞(), 张悟移()   

  1. 昆明理工大学管理与经济学院,昆明 650093
  • 收稿日期:2015-09-08 修回日期:2015-11-23 出版日期:2016-03-25 发布日期:2016-03-21
  • 作者简介:

    作者简介:李瑞,男,四川荣县人,硕士,研究方向为物流与供应链管理.E-mail:li09rui@163.com

  • 基金资助:
    国家自然科学基金项目(71562023)

Energy demand forecast in the logistics sector based on RBF neural networks

LI Rui(), ZHANG Wuyi()   

  1. Faculty of Management and Economics,Kunming University of Science and Technology,Kunming 650093,China
  • Received:2015-09-08 Revised:2015-11-23 Online:2016-03-25 Published:2016-03-21

摘要:

随着中国经济的快速发展,物流业需求快速增长,规模不断扩大,也带来了能源消耗的增长.研究中国物流业能源消费水平以及能源需求,有利于物流业节能工作的开展,缓解能源压力.本文选取了影响物流业能源需求的11个主要因素,基于径向基神经网络对2001-2012年间中国物流业能源需求相关数据进行模拟与仿真,在此基础上对2016年和2020年物流业能源需求量进行了预测,并分析了11个影响因素的重要性和测算了物流业的能源效率.研究结果表明:①2001-2012年间中国物流业能源消耗总量在不断增加,随着物流业的进一步发展,到2020年物流业能源消费总量将达到51261.92万t标准煤;②在解决物流业能源需求预测问题时,RBF神经网络比GM(1,1)预测模型,BP神经网络方法有更高的预测精度;③通过RBF神经网络变量重要性分析发现固定资产投资对物流业能源消费量的影响程度最大;④目前物流业能源效率明显低于全国能源效率,为节约能源,提高能源利用效率,物流业需要转变能源利用方式和发展模式.

关键词: 物流业, 能源需求预测, 能源消费, 能源效率, 径向基神经网络

Abstract:

As economy of China grows rapidly,the logistics sector,by the tremendous needs of the market,is developing quickly and the scale and energy consumption are exploding. Studying energy consumption and demand in the logistics sector is significant in the implementation of energy conservation and ease energy pressure. We screened 11 main factors affecting energy demand in the logistics sector,and then established a model of prediction and simulation of energy demand from 2001 to 2012 on the basis of the radial basis function (RBF)neural network whereby energy demand in the logistics sector from 2016 and 2020 is predicted. We propose some recommendations to improve energy consumption efficiency based on the independent variable important analysis and measure energy efficiency in the logistics sector. We found that total energy consumption of the logistics sector increased continuously from 2001 to 2012. With further development of China's logistics sector,energy demand will keep increasing for years to come and energy consumption will arrive at 51 261.92 million tons in 2020. Compared with a GM (1,1)model and back propagation (BP)neural network,the RBF neural network is better than both in terms of forecast accuracy for the logistics sector. The variable of investment in fixed assets has a deeper impact on energy consumption in the logistics sector than other variables. The energy intensity of the logistics sector is significantly higher than China's GDP,to save energy and improve energy consumption efficiency the logistics sector needs to change energy utilization and development modes.

Key words: logistics sector, energy demand prediction, energy consumption, energy efficiency, radial basis function (RBF)neural network