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Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China

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  • Wu, Wen-Ze
  • Pang, Haodan
  • Zheng, Chengli
  • Xie, Wanli
  • Liu, Chong

Abstract

Accurate electricity consumption forecasting plays a crucial role in electric power systems and is a challenging task due to its complicated mechanism induced by multiple influential factors. To address this problem, this paper develops a novel nonhomogeneous discrete grey model that considers seasonality by introducing a seasonal index into the fractional accumulation generation operator, which is abbreviated as SFNDGM. In addition, the introduction of a one-step ahead rolling mechanism can further improve its prediction performance. To enhance the efficacy, the particle swarm optimization (PSO) algorithm is employed to determine the fractional accumulation order. Subsequently, to demonstrate the effectiveness and superiority of the rolling SFNDGM model, this model is applied to simulate and predict Hubei’s quarterly electricity consumption from 2010Q4 to 2019Q3. The numerical results show that the proposed rolling model has a better performance than the other benchmark models. Therefore, the optimal model is utilized to predict Hubei’s quarterly electricity consumption by 2021, inferring that electricity consumption will continue to increase in the coming years. Based on the attained forecasts, several suggestions are put forward to promote the sustainable development of Hubei’s electricity consumption.

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  • Wu, Wen-Ze & Pang, Haodan & Zheng, Chengli & Xie, Wanli & Liu, Chong, 2021. "Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009622
    DOI: 10.1016/j.energy.2021.120714
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    Cited by:

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    4. Wang, Yong & He, Xinbo & Zhou, Ying & Luo, Yongxian & Tang, Yanbing & Narayanan, Govindasami, 2024. "A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction," Energy, Elsevier, vol. 302(C).
    5. 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).
    6. Weijie Zhou & Huihui Tao & Huimin Jiang, 2022. "Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    7. Huiping Wang & Yi Wang, 2022. "Estimating per Capita Primary Energy Consumption Using a Novel Fractional Gray Bernoulli Model," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    8. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
    9. He, Jing & Mao, Shuhua & Kang, Yuxiao, 2023. "Augmented fractional accumulation grey model and its application: Class ratio and restore error perspectives," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 220-247.
    10. Huiping Wang & Zhun Zhang, 2022. "Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China," IJERPH, MDPI, vol. 19(9), pages 1-22, April.
    11. Muhammad Shahid Mastoi & Hafiz Mudassir Munir & Shenxian Zhuang & Mannan Hassan & Muhammad Usman & Ahmad Alahmadi & Basem Alamri, 2022. "A Critical Analysis of the Impact of Pandemic on China’s Electricity Usage Patterns and the Global Development of Renewable Energy," IJERPH, MDPI, vol. 19(8), pages 1-30, April.

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