运筹与管理 ›› 2018, Vol. 27 ›› Issue (7): 133-143.DOI: 10.12005/orms.2018.0166
崔焕影, 窦祥胜
收稿日期:
2017-03-18
出版日期:
2018-07-25
作者简介:
崔焕影(1992-),女,河北石家庄人,硕士研究生,硕士学位,研究方向为计量经济、经济发展、国际经济与宏观经济;窦祥胜(1963-),男,安徽定远人,教授,博士学位,研究方向为计量经济、经济发展、国际经济与宏观经济。
基金资助:
CUI Huan-ying, DOU Xiang-sheng
Received:
2017-03-18
Online:
2018-07-25
摘要: 由于碳交易市场价格的波动性大及相互影响关系的复杂性,本文试图构建碳价格长期和短期的最优预测模型。考虑到碳交易价格波动的趋势性和周期性特点,基于经验模态分解算法(EMD)、遗传算法(GA)—神经网络(BP)模型、粒子群算法(PSO)—最小二乘支持向量机(LSSVM)模型及由它们构建的组合预测模型,对中国碳市场交易价格进行短期预测和长期预测。实证分析中将影响碳交易价格的不同宏观经济因素和碳价格时间序列因素做为输入变量,分别代入组合模型进行预测。研究结果表明,在短期预测中,EMD-GA-BP模型预测效果优于GA-BP模型和PSO-LSSVM模型;而在长期预测中,组合模型EMD-PSO-LSSVM模型预测效果优于只考虑碳价格波动趋势性或周期性预测效果。
中图分类号:
崔焕影, 窦祥胜. 基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测[J]. 运筹与管理, 2018, 27(7): 133-143.
CUI Huan-ying, DOU Xiang-sheng. Carbon Price Forecasts in Chinese Carbon Trading Market Based on EMD-GA-BP and EMD-PSO-LSSVM[J]. Operations Research and Management Science, 2018, 27(7): 133-143.
[1] Johansson M T. Bio-synthetic natural gas as fuel in steel industry reheating furnaces-a case study of economic performance and effects on global CO2 emissions[J]. Energy, 2013, 57: 699-708. [2] 郭福春,潘锡.碳市场:价格波动及风险测度——基于EU ET S期货合约价格的实证分析[J].财贸经济,2011,(7):110-118. [3] 朱帮助,魏一鸣.基于GMDH-PSO-LSSVM的国际碳市场价格预测[J].系统工程理论与实践,2011,31(12):2264-2271. [4] Ibrahim B M, Kalaitzoglou I A. Why do carbon prices and price volatility change?[J]. Journal of Banking & Finance, 2016, 63: 76-94. [5] Wang Q, Chiu Y H, Chiu C R. Driving factors behind carbon dioxide emissions in China: a modified production-theoretical decomposition analysis[J]. Energy Economics, 2015, 51: 252-260. [6] Zhao Y, Wang S, Zhang Z, Liu Y, Ahmad A. Driving factors of carbon emissions embodied in China-US trade: a structural decomposition analysis[J]. Journal of Cleaner Production, 2016, 131: 678-689. [7] Wang C, Wang F, Zhang X, Yang Y, Su Y, Ye Y, Zhang H. Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang[J]. Renewable and Sustainable Energy Reviews, 2017, 67: 51-61. [8] Xiong L, Shen B, Qi S, Price L, Ye B. The allowance mechanism of China's carbon trading pilots: a comparative analysis with schemes in EU and California[J]. Applied Energy, 2017, 185: 1849-1859. [9] Sá S A D, Daubanes J. Limit pricing and the(in)effectiveness of the carbon tax[J]. Journal of Public Economics, 2016, 139: 28-39. [10] Andersson F N G, Karpestam P. CO2 emissions and economic activity: short-and long-run economic determinants of scale, energy intensity and carbon intensity[J]. Energy Policy, 2013, 61: 1285-1294. [11] Bredin D, Muckley C. An emerging equilibrium in the EU emissions trading scheme[J]. Energy Economics, 2011, 33: 353-362. [12] Li J F, Wang X, Zhang Y X, Kou Q. The economic impact of carbon pricing with regulated electricity prices in China—an application of a computable general equilibrium approach[J]. Energy Policy, 2014, 75: 46-56. [13] Declercq B, Delarue E, D'haeseleer W. Impact of the economic recession on the european power sector's CO2 emissions[J]. Energy Policy, 2011, 39: 1677-1686. [14] Sun W, Xu Y. Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China[J]. Journal of Cleaner Production, 2016, 112: 1282-1291. [15] Pérez-Suárez R, López-Menéndez A J. Growing green? forecasting CO2 emissions with environmental kuznets curves and logistic growth models[J]. Environmental Science & Policy, 2015, 54: 428-437. [16] Mustaffa Z, Yusof Y, Kamaruddin S S. Gasoline price forecasting: an application of LSSVM with improved ABC[J]. Procedia-Social and Behavioral Sciences, 2014, 129: 601-609. [17] Keles D, Scelle J, Paraschiv F, Fichtner W. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks[J]. Applied Energy, 2016, 162: 218-230. [18] Chiroma H, Khan A, Abubakar A I, et al. A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm[J]. Applied Soft Computing, 2016, 48: 50-58. [19] Li G Q, Xu S W, Li Z M. Short-term price forecasting for agro-products using artificial neural networks[J]. Agriculture and Agricultural Science Procedia, 2010, 1: 278-287. [20] Wang X, Wen J, Zhang Y, Wang Y. Real estate price forecasting based on SVM optimized by PSO[J]. Optik-International Journal for Light and Electron Optics , 2014, 125: 1439-1443. [21] Wang M, Chen Y, Tian L, Jiang S, Tian Z, Du R. Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective[J]. Applied Energy, 2016, 175: 109-127. [22] Iskin I, Daim T, Kayakutlu G, Altuntas M. Exploring renewable energy pricing with analytic network process— comparing a developed and a developing economy[J]. Energy Economics, 2012, 34: 882-891. [23] Sun M, Wang Y, Gao C. Visibility graph network analysis of natural gas price: the case of north american market[J]. Physica A: Statistical Mechanics and its Applications, 2016, 462: 1-11. [24] Azadeh A, Babazadeh R, Asadzadeh S M. Optimum estimation and forecasting of renewable energy consumption by artificial neural networks[J]. Renewable and Sustainable Energy Reviews, 2013, 27: 605-612. [25] Wang S, Zhang N, Wu L, Wang Y. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method[J]. Renewable Energy, 2016, 94: 629-636. [26] Kennedy M, Dinh V N, Basu B. Analysis of consumer choice for low-carbon technologies by using neural networks[J]. Journal of Cleaner Production, 2016, 112: 3402-3412. [27] Atsalakis G S. Using computational intelligence to forecast carbon prices[J]. Applied Soft Computing, 2016, 43: 107-116. [28] Zhu B, Wei Y. Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology[J]. Omega, 2013, 41: 517-524. [29] 高杨,李健.基于EMD-PSO-SVM误差校正模型的国际碳金融市场价格预测[J].中国人口. 资源与环境,2014,24(6):163-170. [30] 张晨,杨仙子.基于改进的Grey-Markov对区域碳排放市场价格的预测[J].统计与决策,2016,(9):92-95. [31] Chevallier J. A model of carbon price interactions with macroeconomic and energy dynamics[J]. Energy Economics, 2011, 33: 1295-1312. [32] Gholgheysari Gorjaei R, Songolzadeh R, Torkaman M, Safari M, Zargar G. A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes[J]. Journal of Natural Gas Science and Engineering, 2015, 24: 228-237. [33] 王文波,费浦生,羿旭明.基于EMD与神经网络的中国股票市场预测[J].系统工程理论与实践,2010,(06):1027-1033. [34] 刘春艳,凌建春,寇林元,仇丽霞,武俊青.GA-BP神经网络与BP神经网络性能比较[J].中国卫生统计,2013,30(2):173-176. [35] 黄建国,罗航,王厚军,龙兵.运用GA-BP神经网络研究时间序列的预测[J].电子科技大学学报,2009,38(5):687-692. [36] 李朋林,梁露露.基于BP神经网络的煤炭价格影响因素及预测研究[J].数学的实践与认识,2015,(17):113-126. [37] Gestel T V, Suykens J A K, Baesens B, et al. Benchmarking least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9: 293-300. [38] 阎威武,朱宏栋,邵惠鹤.基于最小二乘支持向量机的软测量建模[J].系统仿真学报,2003,15(10):1494-1496. [39] 王伟,王田苗,魏洪兴.LS-SVM与多层前向网络的非线性回归性能比较[J].系统仿真学报,2008,20(1):256-258. [40] 唐俊.PSO算法原理及应用[J].计算机技术与发展,2010,20(2):213-216. [41] Yu H, Chen Y, Hassan S G, Li D. Prediction of the temperature in a chinese solar greenhouse based on LSSVM optimized by improved PSO[J]. Computers and Electronics in Agriculture, 2016, 122: 94-102. [42] 兰草,李锴.中国碳金融交易体系效率分析[J].经济学家,2014,10(10):77-85. [43] 周建国,刘宇萍,韩博.我国碳配额价格形成及其影响因素研究——基于VAR模型的实证分析[J].价格理论与实践,2016,5:85-88. [44] Byun S J, Cho H. Forecasting carbon futures volatility using GARCH models with energy volatilities[J]. Energy Economics, 2013, 40: 207-221. [45] 赵立祥,胡灿.我国碳排放权交易价格影响因素研究——基于结构方程模型的实证分析[J].价格理论与实践,2016,(7):101-104. [46] 张云.中国碳金融交易价格机制研究[D].吉林大学,2015. [47] 石艳丽,安海忠,高湘昀.基于时序—神经网络模型的我国石油消费预测[J].资源与产业,2011,13(2):37-42. [48] 朱小梅,郭志钢.石油价格预测算法的仿真研究[J].计算机仿真,2011,28(6):361-364. [49] 吴虹,尹华.ARIMA与SVM组合模型的石油价格预测[J].计算机仿真, 2010,27(5):264-266. |
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