| 引用本文: | 孟凡生,李美莹.组合模型在能源需求预测的应用[J].哈尔滨工业大学学报,2013,45(11):106.DOI:10.11918/j.issn.0367-6234.2013.11.018 |
| MENG Fansheng,LI Meiying
.Application of combined model in energy demand prediction[J].Journal of Harbin Institute of Technology,2013,45(11):106.DOI:10.11918/j.issn.0367-6234.2013.11.018 |
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| 摘要: |
| 为科学预测未来我国能源需求,通过文献萃取选取经济发展水平、人口规模、城市化率、产业结构和技术进步水平作为我国能源需求影响因素．运用拉开档次法计算各模型权重,构建ARIMA、多元回归、灰色GM(1,1)和支持向量回归机的组合预测模型,运用该组合模型和目前常用组合预测模型分别计算我国2005-2011年能源需求,并与实际结果进行比较,结果表明:该组合预测模型具有更高的预测精准度. 最后运用该组合模型预测2012-2020年我国能源需求,预测结果表明: 2012-2020年我国能源需求以平均3.42%的年增长率增长,2020年我国能源需求量将比2012年增加30%左右. |
| 关键词: 能源需求 预测 组合模型 |
| DOI：10.11918/j.issn.0367-6234.2013.11.018 |
| 分类号: |
| 基金项目:国家自然科学基金面上资助项目(71072075);国家软科学研究计划资助项目(2010GXQ5D328);黑龙江省社会科学基金资助项目(08D001);黑龙江软科学重点基金资助项目(GB07D104-2). |
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| Application of combined model in energy demand prediction |
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MENG Fansheng, LI Meiying
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(School of Economics and Management, Harbin Engineering University, 150001 Harbin, China)
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| Abstract: |
| To scientifically predict energy demand, the economic development level, population scale, urbanization rate, industrial structure and level of technical progress were selected as the influencing factors of energy demand, the weight of each model based on Deviation Maximization Method was calculated, and the combined model of ARIMA, Multiple Regression, Grey GM(1,1) and Support Vector Regression were established to calculate energy demand from 2005 to 2011 respectively. The result shows that the combined model based on deviation maximization method has higher prediction accuracy.Finally predict China energy demand from 2012 to 2020 based on the combined model, the prediction results show that China energy demand increases at an average growth rate of 3.42% from 2012 to 2020, China energy demand in 2020 will be about 30% more than that in 2012. |
| Key words: energy demand forecast combined model
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