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Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach

Author

Listed:
  • Katarzyna Rudnik

    (Faculty of Production Engineering and Logistics, Opole University of Technology, 45-758 Opole, Poland)

  • Anna Hnydiuk-Stefan

    (Faculty of Production Engineering and Logistics, Opole University of Technology, 45-758 Opole, Poland)

  • Aneta Kucińska-Landwójtowicz

    (Faculty of Production Engineering and Logistics, Opole University of Technology, 45-758 Opole, Poland)

  • Łukasz Mach

    (Faculty of Economics and Management, Opole University of Technology, 45-036 Opole, Poland)

Abstract

Accurate price forecasts on the EU ETS market are of interest to many production and investment entities. This paper describes the day-ahead carbon price prediction based on a wide range of fuel and energy indicators traded on the Intercontinental Exchange market. The indicators are analyzed in seven groups for individual products (power, natural gas, coal, crude, heating oil, unleaded gasoline, gasoil). In the proposed approach, by combining the Principal Component Analysis (PCA) method and various methods of supervised machine learning, the possibilities of prediction in the period of rapid price increases are shown. The PCA method made it possible to reduce the number of variables from 37 to 4, which were inputs for predictive models. In the paper, these models are compared: regression trees, ensembles of regression trees, Gaussian Process Regression (GPR) models, Support Vector Machines (SVM) models and Neural Network Regression (NNR) models. The research showed that the Gaussian Process Regression model turned out to be the most advantageous and its price prediction can be considered very accurate.

Suggested Citation

  • Katarzyna Rudnik & Anna Hnydiuk-Stefan & Aneta Kucińska-Landwójtowicz & Łukasz Mach, 2022. "Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach," Energies, MDPI, vol. 15(21), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8057-:d:957622
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    References listed on IDEAS

    as
    1. Wenjun Chu & Shanglei Chai & Xi Chen & Mo Du, 2020. "Does the Impact of Carbon Price Determinants Change with the Different Quantiles of Carbon Prices? Evidence from China ETS Pilots," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    2. Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
    3. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    4. repec:dau:papers:123456789/4210 is not listed on IDEAS
    5. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    6. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    7. A. C. Christiansen & A. Arvanitakis & K. Tangen & H. Hasselknippe, 2005. "Price determinants in the EU emissions trading scheme," Climate Policy, Taylor & Francis Journals, vol. 5(1), pages 15-30, January.
    8. Shuang Wu & Li He & Zhaolong Zhang & Yu Du, 2021. "Forecast of Short-Term Electricity Price Based on Data Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, February.
    9. 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.
    10. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    11. Yu, Jongmin & Mallory, Mindy L., 2014. "Exchange rate effect on carbon credit price via energy markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 145-161.
    12. Bredin, Don & Muckley, Cal, 2011. "An emerging equilibrium in the EU emissions trading scheme," Energy Economics, Elsevier, vol. 33(2), pages 353-362, March.
    13. Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
    14. Juyong Lee & Youngsang Cho, 2021. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Papers 2107.06174, arXiv.org.
    15. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    16. Chevallier, Julien, 2011. "Nonparametric modeling of carbon prices," Energy Economics, Elsevier, vol. 33(6), pages 1267-1282.
    17. Segnon, Mawuli & Lux, Thomas & Gupta, Rangan, 2017. "Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 692-704.
    18. repec:dau:papers:123456789/6791 is not listed on IDEAS
    19. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
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