“新常态”下的中国天然气消费分析及预测
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摘要
作为一种优质、高效的绿色能源,天然气在中国能源结构中所占比重逐渐增加。但可再生能源的崛起使得天然气成为过渡能源的选择,天然气消费量的增长趋势不明晰,因此相关企业及部门需要合理、准确的天然气需求预测模型为未来的工作提供指导性信息。基于此,本文首先从经济水平、产业结构、能源结构、天然气价格等方面选取影响天然气消费的12个因素。其次,运用贝叶斯模型平均(BMA)法构建了一个包含相关文献中常用的6个影响因素的基准模型,针对该模型,围绕影响天然气消费量的各种因素,以逐个添加的方式建立对比模型,从备选模型中选出预测精度最高的对未来天然气消费量进行预测。最后,将BMA模型与ARIMA模型、ETS模型、BVAR模型、逐步回归模型以及等权重加权平均模型的预测精度进行对比。结果表明,最优的BMA模型包含了涉及经济水平、产业结构、能源结构、人口因素、天然气价格、天然气供给六个方面9个影响因素,其预测精度优于对比预测模型,且该模型预测2022年天然气消费量将达到3254. 153亿立方米,年均增长率为8%。
In this study,the authors aim to establish a reasonable and accurate natural gas forecasting model to provide guidance information for the future work of related companies and departments in the ever-changing and complex background of the natural gas market. Firstly,12 factors affecting natural gas consumption are selected from the aspects of economic level,industrial structure,energy structure,and natural gas price. Secondly,a Bayesian Model Average( BMA) method is used to construct a benchmark model containing six influencing factors commonly used in related literature. On this basis,a set of comparative models is established by adding various factors that affect the consumption of natural gas one by one. Then we select the model with the highest prediction accuracy to predict future natural gas consumption. Finally,in order to detect the prediction effect of the BMA model,it is compared with the ARIMA model,the ETS model,the BVAR model,the STEP model and the equal weighted average model. The results show that the optimal BMA model includes nine factors that affect the economic level,industrial structure,energy structure,population factors,natural gas prices,and natural gas supply. The prediction accuracy is better than the comparison prediction models. The model predicts that natural gas consumption in 2022 will reach 325. 413 billion cubic meters with an average annual growth rate of 8%.
In this study,the authors aim to establish a reasonable and accurate natural gas forecasting model to provide guidance information for the future work of related companies and departments in the ever-changing and complex background of the natural gas market. Firstly,12 factors affecting natural gas consumption are selected from the aspects of economic level,industrial structure,energy structure,and natural gas price. Secondly,a Bayesian Model Average( BMA) method is used to construct a benchmark model containing six influencing factors commonly used in related literature. On this basis,a set of comparative models is established by adding various factors that affect the consumption of natural gas one by one. Then we select the model with the highest prediction accuracy to predict future natural gas consumption. Finally,in order to detect the prediction effect of the BMA model,it is compared with the ARIMA model,the ETS model,the BVAR model,the STEP model and the equal weighted average model. The results show that the optimal BMA model includes nine factors that affect the economic level,industrial structure,energy structure,population factors,natural gas prices,and natural gas supply. The prediction accuracy is better than the comparison prediction models. The model predicts that natural gas consumption in 2022 will reach 325. 413 billion cubic meters with an average annual growth rate of 8%.
引文
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[27] Ma Y,Li Y. Analysis of the supply-demand status of China’s natural gas to 2020[J]. Petroleum Science,2010,7(1):132-135.
[29] He G J,Xiao R G,Liang S. Prediction and influencing factors analysis of natural gas consumption in china based on SPSS[C]//International Conference on Automation,Mechanical Control and Computational Engineering,2015.
[30] Huntington H G. Industrial natural gas consumption in the united states:an empirical model for evaluating future trends[J]. Energy Economics,2007,29(4):743-759.
[31] Hubbert M. Nuclear energy and the fossil fuel[J].American Petroleum Institute Drilling&Production Practice,1970.
[32] Behrouznia A,Saberi M,Azadeh A,et al. An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and analysis:the case of south america[C]//International Conference on Intelligent and Advanced Systems.IEEE,2010. 1-6.
[33]甄仟,郭晓茜,闫强.基于行业视角的中国天然气消费因素分解[J].中国矿业,2018(2).
[34]赵晓琴,康正坤,吴凤荣.天然气消费的影响因素及灰色关联分析[J].油气储运,2008,27(8):5-8.
[35]揣小伟,黄贤金,王倩倩,等.基于信息熵的中国能源消费动态及其影响因素分析[J].资源科学,2009,31(8):1280-1285.
[36]蔡流.我国天然气供需格局演变及影响因素分析[J].地域研究与开发,2014,33(2):41-45.
[37] Wang T,Lin B. China’s natural gas consumption peak and factors analysis:a regional perspective[J]. Journal of Cleaner Production,2016.
[38] Garciadonato G,Forte A. Bayes Var Sel:bayesian testing,variable selection and model averaging in linear models using R[J]. 2016. Mark N C. Exchange Rates and Fundamentals:Evidence on Long-Horizon Predictability[J]. American Economic Review,1995,85(1):201-218.
[39]兰海强,孟彦菊,张炯. 2030年城镇化率的预测:基于四种方法的比较[J].统计与决策,2014(16):66-70.
[40]万广华. 2030年:中国城镇化率达到80%[J].国际经济评论,2011(6):99-111.
[2]张新雨,邹国华.模型平均方法及其在预测中的应用[J].统计研究,2011,28(06):97-102.
[3] Shahbaz M,Lean H H,Farooq A. Natural gas consumption and economic growth in pakistan[J]. Renewable&Sustainable Energy Reviews,2013,18(2):87-94.
[4]Ozge Dilaver,Dilaver Z,Hunt L C. What drives natural gas consumption in europe? analysis and projections[J].Journal of Natural Gas Science&Engineering,2014,19(7):125-136.
[5] Wang T,Lin B. China’s natural gas consumption and subsidies—from a sector perspective[J]. Energy Policy,2014,65(C):541-551.
[6] Rodger J A. A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Syst Appl[J]. Expert Systems with Applications,2014,41(4):1813-1829.
[7] Szoplik J. Forecasting of natural gas consumption with artificial neural networks[J]. Energy, 2015, 85:208-220.
[8] Yu F,Xu X. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network[J]. Applied Energy,2014,134(134):102-113.
[9] Bianco V,Scarpa F,Tagliafico L A. Scenario analysis of nonresidential natural gas consumption in italy[J].Applied Energy,2014,113(6):392-403.
[10] Bianco V,Scarpa F,Tagliafico L A. Analysis and future outlook of natural gas consumption in the Italian residential sector[J]. Energy Conversion&Management,2014,87(87):754-764.
[11]卢全莹,柴建,朱青,邢丽敏,邓俊丽.天然气消费需求分析及预测[J].中国管理科学,2015,23(S1):823-829.
[12] Zeng B,Li C. Forecasting the natural gas demand in China using a self-adapting intelligent grey model[J].Energy,2016,112:810-825.
[13] Wang J,Jiang H,Zhou Q,et al. China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model[J].Renewable&Sustainable Energy Reviews,2016,53(1):1149-1167.
[14] Bates J M,Granger C W J. The combination of forecasts[J]. Journal of the Operational Research Society,1969,20(4):451-468.
[15] Xu G,Wang W. Forecasting China’s natural gas consumption based on a combination model[J]. Journal of Natural Gas Chmistr. 2010,19(5):493-496.
[16]柴建,卢全莹,张钟毓,等.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.
[17]陈伟,牛霖琳.基于贝叶斯模型平均方法的中国通货膨胀的建模及预测[J].金融研究,2013(11):15-27.
[18] Raftery A E,Madigan D,Hoeting J A. Bayesian model averaging for linear regression models[J]. Journal of the American Statistical Association, 1997, 92(437):179-191.
[19] Hoeting J A,Madigan D,Raftery A E,et al. Bayesian model averaging:a tutorial[J]. Statistical Science,1999,14(4):382-401.
[20] Chipman H,George E I,Mcculloch R E,et al. The practical implementation of bayesian model selection[J]. Lecture Notes-Monograph Series, 2001, 38(262):65-134.
[21] Min C K,Zellner A. Bayesian and non-bayesian methods for combining models and forecasts with applications to forecasting international growth rates[J]. Journal of Econometrics,1993,56(1-2):89-118.
[22] Koop G,Potter S. Forecasting in large macroeconomic panels using bayesian model averaging[J]. Staff Reports,2003.
[23] Robert Kass E,Adrian Raftery E. Bayes factors[J].Journal of the American Statistical Association,1995,90(430):773-795.
[24]李兰兰,徐婷婷,李方一,等.中国居民天然气消费重心迁移路径及增长动因分解[J].自然资源学报,2017,32(4):606-619.
[25]滕玉华,刘长进.中国省际技术进步、技术效率与区域能源需求[J].中国人口·资源与环境,2010,20(3):30-34.
[26]柴建,卢全莹,邢丽敏,等.中国天然气产业的发展过快了吗[J].管理评论,2017,29(8):23-32.
[27] Ma Y,Li Y. Analysis of the supply-demand status of China’s natural gas to 2020[J]. Petroleum Science,2010,7(1):132-135.
[29] He G J,Xiao R G,Liang S. Prediction and influencing factors analysis of natural gas consumption in china based on SPSS[C]//International Conference on Automation,Mechanical Control and Computational Engineering,2015.
[30] Huntington H G. Industrial natural gas consumption in the united states:an empirical model for evaluating future trends[J]. Energy Economics,2007,29(4):743-759.
[31] Hubbert M. Nuclear energy and the fossil fuel[J].American Petroleum Institute Drilling&Production Practice,1970.
[32] Behrouznia A,Saberi M,Azadeh A,et al. An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and analysis:the case of south america[C]//International Conference on Intelligent and Advanced Systems.IEEE,2010. 1-6.
[33]甄仟,郭晓茜,闫强.基于行业视角的中国天然气消费因素分解[J].中国矿业,2018(2).
[34]赵晓琴,康正坤,吴凤荣.天然气消费的影响因素及灰色关联分析[J].油气储运,2008,27(8):5-8.
[35]揣小伟,黄贤金,王倩倩,等.基于信息熵的中国能源消费动态及其影响因素分析[J].资源科学,2009,31(8):1280-1285.
[36]蔡流.我国天然气供需格局演变及影响因素分析[J].地域研究与开发,2014,33(2):41-45.
[37] Wang T,Lin B. China’s natural gas consumption peak and factors analysis:a regional perspective[J]. Journal of Cleaner Production,2016.
[38] Garciadonato G,Forte A. Bayes Var Sel:bayesian testing,variable selection and model averaging in linear models using R[J]. 2016. Mark N C. Exchange Rates and Fundamentals:Evidence on Long-Horizon Predictability[J]. American Economic Review,1995,85(1):201-218.
[39]兰海强,孟彦菊,张炯. 2030年城镇化率的预测:基于四种方法的比较[J].统计与决策,2014(16):66-70.
[40]万广华. 2030年:中国城镇化率达到80%[J].国际经济评论,2011(6):99-111.