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A novel compositional data model for predicting the energy consumption structures of Europe, Japan, and China

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  • Xinping Xiao

    (Wuhan University of Technology)

  • Xue Li

    (Wuhan University of Technology)

Abstract

The analysis and prediction of the energy consumption structure provide an important basis for promoting the optimization of the energy consumption structure, energy saving, and emission reduction. The structure data of energy consumption can reflect the proportion of various energy consumptions and belong to component data. In this study, an autoregressive moving average model of component data (CDARMA) based on simplex space and its algebraic system is established to study the energy consumption structure. First, various mathematical concepts in simplex space are presented to lay a theoretical modeling foundation. Second, the autocorrelation and partial autocorrelation coefficients of the component data are defined, and the CDARMA model in the simplex space is established. The parameters are estimated using least squares. Third, the energy consumption structure data of Europe, Japan, and China are used to verify the effectiveness of the proposed model. And the results are compared with other four models under eight kinds of transformation. Overall, the new model has a better fitting effect, prediction effect, and stability than the compared models. Lastly, the CDARMA model is used to predict the energy consumption structure of the three regions in 2019–2025 to provide a reference for the adjustment of the energy structure.

Suggested Citation

  • Xinping Xiao & Xue Li, 2023. "A novel compositional data model for predicting the energy consumption structures of Europe, Japan, and China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11673-11698, October.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:10:d:10.1007_s10668-022-02547-5
    DOI: 10.1007/s10668-022-02547-5
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    References listed on IDEAS

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    1. Zheng, Tingguo & Chen, Rong, 2017. "Dirichlet ARMA models for compositional time series," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 31-46.
    2. L. Kavalieris & E. J. Hannan & M. Salau, 2003. "Generalized Least Squares Estimation Of Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 165-172, March.
    3. Huiwen Wang & Liying Shangguan & Rong Guan & Lynne Billard, 2015. "Principal component analysis for compositional data vectors," Computational Statistics, Springer, vol. 30(4), pages 1079-1096, December.
    4. Yigang Wei & Zhichao Wang & Huiwen Wang & Yan Li & Zhenyu Jiang, 2019. "Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-42, April.
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