基于支持向量机模型的北京市能源需求预测研究  

Energy Demand Projection Based on Support Vector Machine Model:Evidence from Beijing

作　　者：张跃军[1] 周彬[2] 王丽[3] 

机构地区：[1]北京理工大学管理与经济学院能源与环境政策研究中心,北京100081 [2]北京理工大学光电学院,北京100081 [3]中国人民银行营业管理部,北京100045

出　　处：《北京理工大学学报（社会科学版）》2013年第3期8-12,共5页Journal of Beijing Institute of Technology：Social Sciences Edition

基　　金：国家自然科学基金资助项目(71001008;71273028;71020107026);高等学校博士学科点专项科研基金资助项目(20101101120041);教育部人文社会科学研究基金资助项目(09YJC630011);北京市优秀人才培养资助计划(2011D009011000008)

摘　　要：北京市能源需求系统具有非线性、历史数据较少而影响因素众多等复杂特征,而支持向量机模型在解决小样本、非线性及高维模式识别问题方面具有突出优势。为此,引入支持向量机模型对北京市1978-2010年能源需求进行建模,并据此对2012-2020年能源需求量进行预测。结果表明:支持向量机模型能有效拟合北京市能源需求系统的复杂变化趋势,比其他传统方法有更高的预测精度。研究发现,2012-2020年北京市能源需求量逐年增加,年均增速2.75%;另外,北京市能源需求的增速在"十三五"期间会比"十二五"期间略有趋缓。The energy demand system of Beijing has a number of features including nonlinearity,limited historical data and numerous drivers,while the Support Vector Machine（SVM） model owns unique advantage in small samples,nonlinear and high-dimensional pattern recognition.Therefore,the SVM model is employed to fit the related historical data about energy demand in Beijing from 1978—2010 and then project the energy demand during 2012—2020.The results indicate that the projection power of SVM model evidently outweighs that of other traditional and commonly-used models,which can effectively consider the complex features in energy demand system of Beijing.Additionally,the projection results also suggest that Beijing＇s energy demand may increase year by year during 2012—2020,with the average annual growth rate 2.75%;and its growth rate may appear relatively slower in the 13th Five-Year Plan period than that in the 12th Five Year Plan period.

关 键 词：能源需求 支持向量机 预测模型 

分 类 号：F426[经济管理—产业经济] C931[经济管理—管理学]