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基于RBF神经网络的物流业能源需求预测

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

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.