Energy resources are the base of nationaleconomic development and the essentials of human daily life. As the demands of all countries in the world on the energy resources increase, the competition for the energy resources are becoming more and more intensive. It is vital to collect and analysis the energy consumption data of the major countries around the world so that the government could make correct decision on the future national energy consumption using a scientific prediction model. Not similar to the time series commonly used, an adjusted Logistic model, which is based on the classical Logistic model of national energy annual consumption, is founded in this paper by introduceing a factor of the GDP growth ratio The adjusted Logistic model can be considered as a result of the classical logistic model modulated with the GDP growth ratio. Three different numerical models derived from the original adjusted Logistic model are the adjusted analytic model, dynamic differential model and static differential model. Then, the study and verification, which are based on the real statistic data of the energy annual consumption of USA from 1980 to 2010, shows that the fitted and prediction data are in good agreement with the empirical results. The simulation curve is fitted very well with the energy consumption fluctuation. According to the simulation and analysis results in this paper, a positive relevance between the national energy annual consumption and the national economics are shown directive in the analytic model. The relative prediction errors on 2011 and 2012 using the static differential model are only 0.63% and 3.84%, respectively.
YANG Bo, GUO Jian-chuan, TAN Zhang-lu
. Research on Adjusted Logistic Model of National Energy Consumption with Slight Modulation by the Growth Ratio of GDP[J]. Chinese Journal of Management Science, 2017
, 25(6)
: 32
-38
.
DOI: 10.16381/j.cnki.issn1003-207x.2017.06.004
[1] 李子奈,潘文卿. 计量经济学[M]. 第3版. 北京:高等教育出版社,2010.
[2] 杨伟传. 中国能源消费的ARIMA模型预测分析[J]. 统计与决策,2009,(11):71-72.
[3] Yuana Chaoqing, Liu Sifeng, Fang Zhigeng. Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model[J]. Energy, 2016, 100: 384-390.
[4] Ediger V S, Akar S. ARIMA forcasting of primary energy demand by fuel in Turkey[J]. Energy Policy, 2007, 35(3): 1701-1708.
[5] 孙李红,张瑜,焦艳会. 中国能源消费预测的GM(1,1)模型研究[J]. 商业经济,2015,(9):48-49.
[6] 花玲,谢乃明. 政策冲击影响下中国能源消费预测分析及控制策略[J]. 中国管理科学,2014,22(7): 18-25.
[7] Murat Y S, Ceylan H. Use of artificial neural networks for transport energy demad modeling[J]. Energy Policy, 2006, 34(17): 3165-3172.
[8] 王珏,鲍勤. 基于小波神经网络的中国能源需求预测模型[J]. 系统科学与数学. 2009,29(11):1542-1551.
[9] 张俊深,袁程炜. 基于BP神经网络与修正GM(1,1)模型的能源消费组合预测. 统计与决策,2016,(5):90-93.
[10] 卢奇,顾培亮,邱世明. 组合预测模型在我国能源消费系统的构建即应用[J]. 系统工程理论与实践, 2003(3):24-30.
[11] 卢二坡. 组合模型在我国能源需求中的应用[J]. 数理统计与管理, 2006, 25(5):505-510.
[12] 曾波,刘思峰,方志耕,等. 灰色组合预测模型及其应用[J]. 中国管理科学, 2009, 17(5):150-155.
[13] 付娟,金菊良,魏一鸣,等. 基于遗传算法的中国情结能源需求Logistic预测模型[J]. 水电能源科学,2010,28(9):175-178.
[14] 杨波,谭章禄. 基于Logistic模型的中国国家能源消费总量预测研究[J]. 科技管理研究, 2013,33(12):45-48.
[15] U.S. Energy Information Administration (EIA). Annual energy review[EB/DL]. Http://www.eia.gov/totalenergy/data/annual.
[16] Stern D I. A multivariate cointegration analysis of the role of energy in the US macroeconomy[J]. Energy Economics, 2000, 22 (2):267-83.
[17] Huang B N, Hwang M J, Yang C W. Causal relationship between energy consumption and GDP growth revised: A dynamic panel data approach[J]. Ecological Economics, 2008, 67 (1): 41-54.
[18] Kraft J, Kraft A. On the relationship between energy and GNP[J]. The Journal of Energy and Development, 1978, 3 (2):401-403.
[19] Sharma A, Bruce C. The relationship between energy and U.S. GDP:A multivariate vector error-correction model[J]. The Journal of Energy and Development, 2013, 38(1):45-64.
[20] Tiwari A K. The asymmetric Granger-causality analysis between energy consumption and income in the United States[J]. Renewable & Sustainable Energy Reviews, 2014, 36:362-369.
[21] 张优智,党兴华. 能源消费结构与经济增长的动态关联分析[J]. 中国管理科学,2014,22(SI):840-845.
