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Mixed-frequency grey prediction model with fractional lags for electricity demand and estimation of coal power phase-out scale

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  • Gou, Xiaoyi
  • Mi, Chuanmin
  • Zeng, Bo

Abstract

Accurate medium-term and long-term electricity demand forecasting is essential for a structured phase-out of coal power plants and the advancement of a low-carbon power sector. To this end, a novel fractional lag-based mixed-frequency discrete grey model (FMDGM(1,N)) that integrates high-frequency data through the Nakagami function is proposed, enabling comprehensive utilization of multi-frequency features and addressing the limitations of traditional single-frequency electricity demand forecasting frameworks. Unlike conventional mixed-frequency grey prediction models relying on integer lag parameters, the proposed model introduces mathematical functions to capture developmental trends between adjacent time points, successfully extending integer lag parameters into the fractional domain. This innovation enhances model performance and allows for more accurate representation of lag effects among electricity demand drivers. Experimental results demonstrate the model's superior performance and robustness across various data scenarios, significantly outperforming other grey prediction models, regression models, and neural network models in electricity demand forecasting. The forecast indicates that China's electricity demand will reach 11816 TWh by 2030, with a coal power capacity of 1238 GW. This study provides a robust tool for energy planning and low-carbon transition.

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  • Gou, Xiaoyi & Mi, Chuanmin & Zeng, Bo, 2025. "Mixed-frequency grey prediction model with fractional lags for electricity demand and estimation of coal power phase-out scale," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225010849
    DOI: 10.1016/j.energy.2025.135442
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    1. Yang, Lin & Xu, Mao & Fan, Jingli & Liang, Xi & Zhang, Xian & Lv, Haodong & Wang, Dong, 2021. "Financing coal-fired power plant to demonstrate CCS (carbon capture and storage) through an innovative policy incentive in China," Energy Policy, Elsevier, vol. 158(C).
    2. Ma, Kai & Nie, Xuefeng & Yang, Jie & Zha, Linlin & Li, Guoqiang & Li, Haibin, 2025. "A power load forecasting method in port based on VMD-ICSS-hybrid neural network," Applied Energy, Elsevier, vol. 377(PB).
    3. Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
    4. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    5. Wang, Delu & Mao, Jinqi & Shi, Xunpeng & Li, Chunxiao & Chen, Fan, 2024. "A planning model for coal power exit scales based on minimizing idle and shortage losses: A case study of China," Energy Economics, Elsevier, vol. 138(C).
    6. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
    7. Li, Biao & Xie, Bai-Chen & Yu, Xiao-Chen & She, Zhen-Yu & Hu, Wenhao, 2025. "Does the incentive policy for renewable energy grid connection affect the technical efficiency of power grid companies? Empirical analysis based on China and Japan," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 28-47.
    8. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
    9. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
    10. Wang, Yong & Yang, Rui & Zhang, Juan & Sun, Lang & Xiao, Wenlian & Saxena, Akash, 2024. "A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production," Energy, Elsevier, vol. 291(C).
    11. Zeng, Bo & Yin, Fengfeng & Yang, Yingjie & Wu, You & Mao, Cuiwei, 2023. "Application of the novel-structured multivariable grey model with various orders to forecast the bending strength of concrete," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    12. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).
    13. Vaninsky, Alexander, 2007. "Erratum to "Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis" [Energy Economics, 28(2006), 326-338]," Energy Economics, Elsevier, vol. 29(3), pages 1-1, May.
    14. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
    15. Ruan, Guangchun & Wu, Jiahan & Zhong, Haiwang & Xia, Qing & Xie, Le, 2021. "Quantitative assessment of U.S. bulk power systems and market operations during the COVID-19 pandemic," Applied Energy, Elsevier, vol. 286(C).
    16. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    17. Niu, Dongxiao & Ji, Zhengsen & Li, Wanying & Xu, Xiaomin & Liu, Da, 2021. "Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization," Energy, Elsevier, vol. 234(C).
    18. Zeng, Bo & He, Chengxiang & Mao, Cuiwei & Wu, You, 2023. "Forecasting China's hydropower generation capacity using a novel grey combination optimization model," Energy, Elsevier, vol. 262(PA).
    19. Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
    20. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
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