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

Xinping Xiao and Xue Li ()
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Xinping Xiao: Wuhan University of Technology
Xue Li: Wuhan University of Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2023, vol. 25, issue 10, No 44, 11673-11698

Abstract: 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.

Keywords: Energy consumption structure; Component data; Simplex space; ARMA (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10668-022-02547-5

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