Analysis and Forecasting of Power Demand in Industrialization, Urbanization Process

doi:10.12005/orms.2015.0023

Abstract

Abstract: With the development of the industrialization,the urbanization process is accelerating.China’s power demand will continue to rise. The power supply is an important guarantee for the steady development of our economy. Therefore, a reasonable and accurate analysis of electricity demand and forecast is of important practical significance.This paper uses path analysis for screening the core of the electric power consumption demand based on this analysis of the current situation of China’s power demand. We analyze and forecast power demand based on single variables(ETS, ARIMA)and multivariate(scenario analysis)two dimensions based on model selection. The results show that GDP for every 1% increase in the electricity demand in 0.5249%; the level of industrialization for every 1% increase in power demand has increased by 2.2146%, the level of urbanization for every 1% increase in power demand has increased by 1.0076%. At the end of “Twelfth Five-Year Plan”, the consumption capacity of Chinese electric power reaches nearly 61425.96KW/h. By 2020,China electric power consumption will reach nearly 81410.10KW/h.

Key words: scientific forecasting, ETS, ARIMA, path analysis, power demand

摘要： 随着工业化、城镇化进程的不断加快,我国电力需求量将持续上升。电力的充足供应是我国经济稳步发展的重要保证,故合理准确的对电力需求进行分析及预测具有重要的现实意义。基于此,分析我国电力需求现状,利用通径分析筛选电力消费需求的核心驱动因素。在模型选择的基础上,基于单变量(ETS、ARIMA模型)和多变量(情景分析)两个维度进行电力需求量分析及预测。结果表明:GDP每提高1%使得电力需求量提高0.5249%;工业化水平每提高1%使得电力需求量提高2.2146%,城镇化水平每提高1%使电力需求量相应提高1.0076%。“十二五”末中国电力消费需求量将近61425.96KW/h,2020年中国电力消费需求将近81410.10KW/h。

关键词: 预测科学, ETS, ARIMA, 通径分析, 电力需求

CLC Number:

F062.1

CHAI Jian, LU Quan-ying , ZHANG Zhong-yu, WANG Shou-yang. Analysis and Forecasting of Power Demand in Industrialization, Urbanization Process[J]. Operations Research and Management Science, 2015, 24(1): 164-172.

柴建, 卢全莹, 张钟毓, 汪寿阳. 工业化、城镇化进程中电力需求分析及预测[J]. 运筹与管理, 2015, 24(1): 164-172.

References

[1] Kraft J, Kraft A. Relationship between energy and GNP[J]. Journal of Energy and Development, 1978, (3): 401-403.
[2] Julian Silk I, Frederick Joutz L. Short and long-run elasticities in US residential elect ricity demand: a cointegration approach[J]. Energy Economics, 1997, 19(4): 493-513.
[3] Thoma M. Electrical energy usage over the business cycle[J]. Energy Economics, 2004, 26(3): 463-485.
[4] Steenhof P A, Fulton W. Factors affecting electricity generation in China: current situation and prospects[J]. Technological Forecasting and Social Change, 2007, 74(5): 663-681.
[5] 林伯强.电力消费与中国经济增长:基于生产函数的研究[J].管理世界,2003,(11):18-27.
[6] 王海鹏,田澎,靳萍.中国能源消费、经济增长间协整关系和因果关系的实证研究——以电力行业为例[J].生产力研究,2005,(3):159-177.
[7] 朱忠烈,杨宗麟,程浩忠,顾洁,秦康平,林佳,陈银峰.节能减排背景下电力需求分析预测研究[J].华东电力,2009,37(5):0703-0707.
[8] Yukun Bao, Tao Xiong, Zhongyi Hu. Multi-step-ahead time series prediction using multiple-output support vector regression[J]. Neurocomputing, 2013, (17): 1-12.
[9] Badri MA, Al-Mutawa A, Davis D, Davis D. EDSSF: a decision support system(DSS) for electricity peak-load forecasting[J]. Energy, 1997, 22(6): 579-589.
[10] Zachariadis T, Pashourtidou N. An empirical analysis of electricity consumption in Cyprus[J]. Energy Economics, 2007, 29(2): 183-198.
[11] Saab S, Badr E, Nasr G. Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon[J]. Energy, 2001, 26(1): 1-14.
[12] 何晓萍,刘希颖,林艳苹.中国城市化进程中的电力需求预测[J].经济研究,2009,(1):118-130
[13] 林伯强.中国重化工业之行的能源和环境约束[EB/OL].2005-10-16,http://www.21page.net/html/2005-10-18/17393.htm.
[14] 林伯强.城市化是能源可持续问题的关键[EB/OL].21世纪经济报道,2008-11-17,http://opinion.hexun.com/2008-11-15P111259680.html.
[15] Lisa Bianchi, Jeffrey Jarrett R. Choudary Hanumara. Improving forecasting for telemarketing centers by ARIMA modeling with intervention[J]. International Journal of Forecasting, 1998, 14(4): 497-504.
[16] 林伯强.结构变化、效率改进与能源需求预测:以中国电力行业为例[J].经济研究,2003,(5):57-93.
[17] 吴玉鸣,李建霞.省域经济增长与电力消费的局域空间计量经济分析[J].地理科学,2009,29(1):30-35.
[18] Madlener R, Sunak Y. Impacts of urbanization on urban structures and energy demand: what can we learn for urban energy planning and urbanization management[J]. Sustainable Cities and Society, 2011, (1): 45-53.
[19] Jian Chai, Ju-E Guo, Lei Meng, Shou-yang Wang. Exploring the core factors and its dynamic effects on oil price: an application on path analysis and BVAR-TVP model[J]. Energy Policy, 2011, 39(12): 8022-8036.
[20] Jeong Ryeol, Kurz Kim. Combining forecasts using optimal combination weight and generalized autoregression[J]. Journal of Forecasting, 2008, 27(5): 419-432.