Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model

Congjun Rao, Yue Zhang, Jianghui Wen, Xinping Xiao and Mark Goh

Energy, 2023, vol. 263, issue PC

Abstract: Analyzing the drivers of energy demand and predicting energy consumption can help to shape national policies on energy transformation and energy security. This paper estimates the demand for the various forms of energy consumed in China. A least absolute shrinkage and selection operator-random forest (Lasso-RF) two-stage model is first proposed to identify the drivers of the demand in energy. A support vector regression-compositional data second exponential smoothing (SVR-CDSES) model is then established to forecast the demand for coal, oil, natural gas, and primary electricity. The empirical analysis results inform that China's appetite for energy in the next decade will grow at an annualised rate of 2.68%. The demand for coal will rise initially and then fall at a rate of 0.79% per annum. The annual growth rate in the demand for oil will be relatively stable at 3.52%. However, the demand for natural gas and primary electricity will increase rapidly, with the highest annual growth rate at 8.05%, pointing to greater awareness of clean energy.

Keywords: Energy demand; Lasso-RF model; SVR-CDSES forecasting; Compositional data model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222028419

DOI: 10.1016/j.energy.2022.125955

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