基于最优加权组合模型的煤炭消费预测分析
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摘要
为了研究最优的煤炭消费预测模型,为我国能源结构优化提供依据,基于差分自回归移动平均(ARIMA)、灰色预测(GM)和人工神经网络(ANN)模型构建了8个组合预测模型,对我国煤炭消费量进行预测分析,应用评价指标R、MAE、MAPE和RMSE对预测模型精度进行比较,筛选出最优组合模型并预测分析未来10年我国煤炭消费趋势。研究结果表明:(1)最优加权组合模型均方根误差、平均绝对误差、平均相对误差等参数均较小,预测效果明显优于单项和简单组合预测模型;(2)构建了权重为(0.73,0.09,0.18)的我国煤炭消费预测最优加权组合模型ARIMA-GM-ANN。(3)将煤炭消费增长趋势分为"缓慢上升期"、"急速增长期"、"下降期"和"平稳期"四个阶段,2013年煤炭消费量达峰,约43.14亿t,2020年以后,煤炭消费量稳定在35.5亿t左右。
To provide the basis for optimization of China's energy structure,coal consumption forecasting model is studied.Based on ARIMA,GM and ANN,3 single forecasting models and 8 compound forecasting models are built to forecast the coal consumption of China. To forecast and analyze the development trend of China' s coal consumption in next 10 years,the optimal model would be selected through parameter evaluation of R,MAE,MAPE and RMSE. The results show that the parameters MAE,MAPE and RMSE of optimal combination weighting model are smaller,and the prediction effect is obviously better than single prediction and simple combination model. The weight of optimum weighted composition model ARIMA-GM-ANN for China' s coal consumption is( 0. 73,0. 09,0. 18). The growth trend of coal consumption is divided into three stages: "slow rise", "rapid growth period", "declining period"and "stable period ". Coal consumption peaked at about 4. 314 billion tons in 2013. After 2020,coal consumption stabilized at about 3. 55 billion tons.
To provide the basis for optimization of China's energy structure,coal consumption forecasting model is studied.Based on ARIMA,GM and ANN,3 single forecasting models and 8 compound forecasting models are built to forecast the coal consumption of China. To forecast and analyze the development trend of China' s coal consumption in next 10 years,the optimal model would be selected through parameter evaluation of R,MAE,MAPE and RMSE. The results show that the parameters MAE,MAPE and RMSE of optimal combination weighting model are smaller,and the prediction effect is obviously better than single prediction and simple combination model. The weight of optimum weighted composition model ARIMA-GM-ANN for China' s coal consumption is( 0. 73,0. 09,0. 18). The growth trend of coal consumption is divided into three stages: "slow rise", "rapid growth period", "declining period"and "stable period ". Coal consumption peaked at about 4. 314 billion tons in 2013. After 2020,coal consumption stabilized at about 3. 55 billion tons.
引文
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[24]田翔宇.产业集群区域人才需求预测的组合模型研究[D].长沙:中南大学,2009.
[2]张金锁,冯雪,邹绍辉.基于趋势组合的我国煤炭需求预测模型研究[J].商业研究,2014,6(9):51-56.
[3]Silberglitt Richard,Anders Hove,Peter Shulman.Analysis of US energy scenarios:Metascenarios,pathways,and policy implications[J].Technological Forecasting&Social Change,2003,70(4):297-315.
[4]Weber Christoph,Adriaan Perrels.Modelling lifestyle effects on energy demand and related emissions[J].Energy Policy,2000,28(8):549-566.
[5]张会新,白嘉.基于三角灰色系统模型的煤炭消费预测[J].统计与决策,2011(23):38-40.
[6]冯乐,窦鲁星,陈英东.改进灰色GM(1,1)模型在煤炭消费预测中的应用[J].山西焦煤科技,2012(7):34-37.
[7]周晓明,罗文柯,李润球.改进GM(1,1)预测模型对我国煤炭消费需求的预测分析[J].矿业工程研究,2010,25(2):65-68.
[8]郭亮.中国能源消费量预测及方法比较[J].商场现代化,2007(11):268-269.
[9]马国旗,李凯明.基于ARIMA的江苏省煤炭消费预测[J].信息系统工程,2012(7):101-102.
[10]王晓丽.ARIMA模型在中国人均煤炭消费预测中的应用[J].现代经济信息,2012(12):272-273.
[11]张正球,陈娅.基于BP神经网络的我国煤炭消费和碳排放量预测[J].湖南大学学报(社会科学版),2015,29(1):64-67.
[12]施云清,余朋林.基于Matlab技术的双隐含层BP神经网络煤炭需求预测研究—以福建省煤炭需求为例[J].物流工程与管理,2014,36(11):145-147.
[13]Bates J M,Granger C W J.The combination of forecasts[J].Operational Research Quarterly,1969(20):451-468.
[14]Makridakis,S.et al.The accuracy of major ectrapolation methods.Vol,111-113.
[15]G.Peter Zhang,Time series forecasting using a hybrid ARIMA and neural network model[J].Neurocomputing,2003(50):159-175.
[16]Huseyin Ince,Theodore B.Trafalis.A hybrid model for exchange rate prediction[J]Decision Support Systems,2006(42):1054-1062.
[17]Zhongsheng Hua,Bin Zhang.A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts[J].Applied Mathematics And Computation,2006(181):1035-1048
[18]Granger,Amanathan r,Hybrid neural network models for hydrologic time series forecasting[M].Applied Soft Computing,2006
[19]樊正中,邹杰龙.基于组合预测模型的煤炭消费预测分析[J].中国高新技术企业,2012(24):5-7.
[20]吕占海.基于组合预测模型的煤炭消耗预测研究[J].中国煤炭,2012,38(11):12-15.
[21]刘爱芹.基于组合模型的能源消费预测研究[J].中国人口·资源与环境,2010,20(11):25-29.
[22]颜筱红.基于IOWGA算子的能源消费组合预测模型[J].西南民族大学学报·自然科学版,2011,37(4):543-547.
[23]朱峰,高林.基于组合模型的卷烟市场需求预测研究[J].合作经济与科技,2017(1):62-64.
[24]田翔宇.产业集群区域人才需求预测的组合模型研究[D].长沙:中南大学,2009.