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Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China

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  • Zhu, Xiaoyue
  • Dang, Yaoguo
  • Ding, Song

Abstract

The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu’s electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu’s electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers.

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  • Zhu, Xiaoyue & Dang, Yaoguo & Ding, Song, 2020. "Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219321127
    DOI: 10.1016/j.energy.2019.116417
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    18. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
    19. Wang, Qi & Suo, Ruixia & Han, Qiutong, 2024. "A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis," Energy, Elsevier, vol. 292(C).
    20. Ding, Song & Li, Ruojin & Wu, Shu & Zhou, Weijie, 2021. "Application of a novel structure-adaptative grey model with adjustable time power item for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 298(C).
    21. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2021. "Point and interval forecasting of electricity supply via pruned ensembles," Energy, Elsevier, vol. 232(C).
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    23. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).
    24. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).

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