基于GMDH混合模型的能源消费量预测研究

doi:10.16381/j.cnki.issn1003-207x.2017.12.017

中国管理科学 ›› 2017, Vol. 25 ›› Issue (12): 158-166.doi: 10.16381/j.cnki.issn1003-207x.2017.12.017

基于GMDH混合模型的能源消费量预测研究

肖进¹, 孙海燕¹, 刘敦虎², 曹瀚文¹, 汪寿阳³

1. 四川大学商学院, 四川成都 610064;
2. 成都信息工程学院管理学院, 四川成都 610225;
3. 中国科学院数学与系统科学研究院, 北京 100190

收稿日期:2015-06-04 修回日期:2017-08-25 出版日期:2017-12-20 发布日期:2018-02-10
通讯作者: 刘敦虎(1976-),男(汉族),四川攀枝花人,成都信息工程大学管理学院,教授,管理学博士,研究方向:创新管理,E-mail:264885613@qq.com E-mail:264885613@qq.com
基金资助:
国家自然科学基金资助项目（71471124，71273036，71673011）；四川大学青年杰出人才培育项目（sksyl201709）；四川大学优秀青年基金项目（skqy201607）；四川大学一流大学建设人才专项（skzx2016-rcrw14）

GMDH Based Hybrid Model for China's Energy Consumption Prediction

XIAO Jin¹, SUN Hai-yan¹, LIU Dun-hu², CAO Han-wen¹, WANG Shou-yang³

1. Business School, Sichuan University, Chengdu 610064, China;
2. Management Faculty, Chengdu University of Information Technology, Chengdu 610225, China;
3. Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190, China

Received:2015-06-04 Revised:2017-08-25 Online:2017-12-20 Published:2018-02-10

摘要/Abstract

摘要： 能源是人类社会赖以生存和发展的物质基础，在国民经济中具有重要的战略地位。随着社会经济的发展，能源的需求越来越大，因此需要对能源的使用进行有效的管理。能源消费预测是能源供需管理的理论前提，建立可靠的能源消费预测模型显得尤为重要。目前，已有的能源消费量预测模型主要包括单一模型和混合模型两大类。本研究提出了基于数据分组处理（group method of data handling，GMDH）的混合预测模型GHFM。该模型首先使用基于GMDH的自回归模型在原始能源消费时间序列上建模，预测其线性趋势，并得到残差序列（非线性子序列）。考虑到非线性子序列预测的复杂性，分别建立BP神经网络、支持向量回归机、遗传规划和RBF神经网络模型，再运用GMDH在非线性子序列上建立选择性组合预测模型，得到非线性子序列的组合预测值。最后，将两个部分的预测值进行整合得到总的能源消费量预测值。选取中国统计年鉴2014能源统计数据中的中国能源消费总量和石油消费总量数据进行实证分析，结果表明，GHFM模型与其他模型相比具有更好的预测效果。最后，给出了使用GHFM模型对2015-2020年中国能源消费总量的样本外预测值。

关键词: 能源消费量, GMDH, 混合预测, 选择性组合预测

Abstract: Energy is the material basis for the survival and development of human society and it plays an important strategic role in national economy.With the development of society, it shows an increasing demand of energy, so effective management is important for energy use. Energy consumption prediction is the theory premise of energy supply and demand management, therefore, establishing a reliable energy consumption forceasting model is particularly significant. At present, the existing models mainly includes two types:single prediction models and hybrid prediction models.In this study' a group method of data handling (GMDH) based hybrid forecasting model (GHFM) is proposed for China's energy consumption prediction. In this model, GMDH based auto-regression is first constructed in the original energy consumption time series, the linear trend of time series is predicted, and residual series (i.e., non-linear sub-series) is obtained. Considering the complexity of non-linear sub-series prediction, BP neural network, support vector regression, genetic programming and RBF neural network are constructed respectively, and then combined forecasting model is condueted selectively by GMDH in non-linear sub-series and the combination prediction are obtained for the non-linear sub-series. At last, the total energy consumption forecast values are integrated from the two parts above. Empirical analysis is conducted on the total energy consumption and oil consumption time series from China Statistical Yearbook of energy statistics data (2014) and the results show that the forecast performance of GHFM model is better than other models. In addition, the out-of-sample forecasts of China's total energy consumption from 2015 to 2020 is given based on GHFM model. The proposed model can be used to improve the accuracy of energy consumption forecasting. The model can also be applied to other time series forecasting problems, including container throughput forecasting, crude oil price forecasting, electricity load forecasting, stock price forecasting, et al.

Key words: energy consumption, GMDH, hybrid forecasting, combined forecasting

中图分类号:

TK01+8

肖进, 孙海燕, 刘敦虎, 曹瀚文, 汪寿阳. 基于GMDH混合模型的能源消费量预测研究[J]. 中国管理科学, 2017, 25(12): 158-166.

XIAO Jin, SUN Hai-yan, LIU Dun-hu, CAO Han-wen, WANG Shou-yang. GMDH Based Hybrid Model for China's Energy Consumption Prediction[J]. Chinese Journal of Management Science, 2017, 25(12): 158-166.

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基于GMDH混合模型的能源消费量预测研究

GMDH Based Hybrid Model for China's Energy Consumption Prediction

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