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Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model

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  • Xiong, Pingping
  • Li, Kailing
  • Shu, Hui
  • Wang, Junjie

Abstract

To forecast natural gas consumption more accurately, to clearly understand the future supply situation, and to optimize the allocation of resources, a new fractional-order accumulation-based incomplete gamma grey forecasting model is proposed in this paper. To further optimize the traditional grey action quantity, dynamic nonlinear action-based incomplete gamma functions are taken as the grey action quantity and combined with fractional-order accumulation. The role of new information is fully considered, and a detailed modeling process is presented, including computational steps and intelligent optimization algorithms. In this study, this new model is used to simulate and forecast natural gas consumption in the Asia-Pacific region from 2008 to 2018. First, Bangladesh and the Philippines are taken as examples to show the error changes incurred by the model under the control of two parameters. Then, a simulation and prediction of natural gas consumption are conducted and compared with those of other traditional univariate grey models. The results show that the MAPE obtained by the new model is the lowest, which indicates the prediction accuracy and effectiveness of the model. This model has good prediction performance for natural gas consumption and can be extended to more energy consumption prediction problems.

Suggested Citation

  • Xiong, Pingping & Li, Kailing & Shu, Hui & Wang, Junjie, 2021. "Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model," Energy, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s0360544221017813
    DOI: 10.1016/j.energy.2021.121533
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    3. Xu, Guangyue & Chen, Yaqiang & Yang, Mengge & Li, Shuang & Marma, Kyaw Jaw Sine, 2023. "An outlook analysis on China's natural gas consumption forecast by 2035: Applying a seasonal forecasting method," Energy, Elsevier, vol. 284(C).
    4. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    5. Weijie Zhou & Huihui Tao & Jiaxin Chang & Huimin Jiang & Li Chen, 2023. "Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    6. Longfeng Zhang & Xin Ma & Hui Zhang & Gaoxun Zhang & Peng Zhang, 2022. "Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom," Energies, MDPI, vol. 15(19), pages 1-26, October.
    7. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
    8. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
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    10. 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.

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