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Forecasting China's hydropower generation capacity using a novel grey combination optimization model

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  • Zeng, Bo
  • He, Chengxiang
  • Mao, Cuiwei
  • Wu, You

Abstract

Hydropower is the largest renewable energy power generation source with the largest construction scale and power generation capacity. A reasonable prediction of hydropower generation is conducive to achieving the carbon peak and neutrality goals. Hydropower generation is affected by seasons and precipitation, which have significant randomness and uncertainty. To realize a reasonable prediction of hydropower generation in China, this paper constructs a novel grey combination optimization model using different parameter combination optimizations based on the three-parameter discrete grey model TDGM(1,1). Further research shows that a better model performance can not necessarily achieved by a higher number of parameter combination optimizations, mainly due to the different effects and influence of various parameters on the model. Subsequently, the TDGM(1,1,r,ξ,Csz) model is applied to forecast China's hydropower generation. The results show that China's hydropower generation can reach 1687.738 hundred million kWh in 2025, an increase of 24.5% compared with 2020. Finally, the rationality of the prediction results is analyzed, and relevant countermeasures and suggestions are proposed.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222022241
    DOI: 10.1016/j.energy.2022.125341
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Shuai & Wang, Yimin & Su, Hui & Chang, Jianxia & Huang, Qiang & Li, Ziyan, 2024. "Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations," Energy, Elsevier, vol. 299(C).
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    3. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).

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