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A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

Author

Listed:
  • Guoqiang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Tong Chen

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Zhinong Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Yonghui Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Haixiang Zang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Sheng Chen

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on variational mode decomposition (VMD) and spiking neural networks (SNNs) is proposed. An original carbon price series is firstly decomposed into a series of relatively stable components through VMD to simplify the interference and coupling across characteristic information of different scales in the data. Then, a SNN forecasting model is built for each component, and the partial autocorrelation function (PACF) is used to determine the input variables for each SNN model. The final forecasting result for the original carbon price can be obtained by aggregating the forecasting results of all the components. Actual InterContinental Exchange (ICE) carbon price data is used for simulation, and comprehensive evaluation criteria are proposed for quantitative error evaluation. Simulation results and analysis suggest that the proposed VMD-SNN forecasting model outperforms conventional models in terms of forecasting accuracy and reliability.

Suggested Citation

  • Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:54-:d:62458
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    References listed on IDEAS

    as
    1. Jun Yang & Xin Feng & Yufei Tang & Jun Yan & Haibo He & Chao Luo, 2015. "A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers," Energies, MDPI, vol. 8(9), pages 1-20, August.
    2. repec:dau:papers:123456789/4222 is not listed on IDEAS
    3. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    4. Seifert, Jan & Uhrig-Homburg, Marliese & Wagner, Michael, 2008. "Dynamic behavior of CO2 spot prices," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 180-194, September.
    5. Ye Liu & Guohe Huang & Yanpeng Cai & Cong Dong, 2011. "An Inexact Mix-Integer Two-Stage Linear Programming Model for Supporting the Management of a Low-Carbon Energy System in China," Energies, MDPI, vol. 4(10), pages 1-30, October.
    6. repec:dau:papers:123456789/4210 is not listed on IDEAS
    7. Guo-Feng Fan & Shan Qing & Hua Wang & Wei-Chiang Hong & Hong-Juan Li, 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-15, April.
    8. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect," Applied Energy, Elsevier, vol. 87(6), pages 1804-1814, June.
    9. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    10. Chevallier, Julien, 2009. "Carbon futures and macroeconomic risk factors: A view from the EU ETS," Energy Economics, Elsevier, vol. 31(4), pages 614-625, July.
    11. Alberola, Emilie & Chevallier, Julien & Cheze, Benoi^t, 2008. "Price drivers and structural breaks in European carbon prices 2005-2007," Energy Policy, Elsevier, vol. 36(2), pages 787-797, February.
    12. Lahmiri, Salim, 2015. "Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 130-138.
    13. Zhu, Y. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2015. "A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty," Energy, Elsevier, vol. 88(C), pages 636-649.
    14. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    15. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2015. "Carbon Price Analysis Using Empirical Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 195-206, February.
    16. Dégerine, Serge & Lambert-Lacroix, Sophie, 2003. "Characterization of the partial autocorrelation function of nonstationary time series," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 46-59, October.
    17. Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
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