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Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models

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  • Meng, Ming
  • Wang, Lixue
  • Shang, Wei

Abstract

In the present “new normal” economic mode, the household is a major driver of China's electricity consumption growth. To guide the development of the electric power industry in adapting to this situation, this study used the household electricity consumption and population data of 30 provinces during 2001–2014, a three-dimensional decomposition model, and a hybrid trend extrapolation model to explore the driving factors of China's household electricity consumption growth and forecast its future development trend before 2030. Empirical analysis drew the following conclusions: (1) China's household electricity consumption growth is mainly attributed to the improvement of its living standards and still has great potential. (2) Population increase and provincial population structure adjustment have little impact on household electricity consumption growth. (3) In 2030, China's household electricity consumption per capita will increase to 1.06 thousand kWh per capita. (4) China's household electricity consumption will increase to 1.57 trillion kWh in 2030, which is twice that in 2015. The implementation of the universal two-child population policy will have no significant impact on these forecasting results. (5) Raising household electric price level, setting cool and heat storage price, and developing the micro-grid are the suggested policy directions.

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  • Meng, Ming & Wang, Lixue & Shang, Wei, 2018. "Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models," Energy, Elsevier, vol. 165(PA), pages 143-152.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pa:p:143-152
    DOI: 10.1016/j.energy.2018.09.090
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    7. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    8. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
    9. Peng Jiang & Jun Dong & Hui Huang, 2019. "Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm," Energies, MDPI, vol. 12(7), pages 1-24, April.
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