IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v215y2021ipas0360544220322258.html
   My bibliography  Save this article

Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization

Author

Listed:
  • Li, Nu
  • Wang, Jianliang
  • Wu, Lifeng
  • Bentley, Yongmei

Abstract

Accurate prediction of short and medium-term monthly natural gas production in a country is the basis for understanding the supply capacity of natural gas in different months, and for the timely adjustment of natural gas production and import strategies. In China the monthly production of natural gas has obvious seasonal and cyclical variations, thus the use of a traditional grey prediction model is not very effective. As a result, a novel grey seasonal model is proposed in this paper. This is the Particle swarm optimized Fractional-order-accumulation non-homogenous discrete grey Seasonal Model (PFSM(1,1) model). This model enhances the adaptability to seasonal fluctuation data in two ways: the seasonal adjustment of the original data, and improvement of model self-adaptability. We use monthly natural gas production data of China for the period 2013–2018 as samples to predict those for the period 2019–2023. To demonstrate the PFSM(1,1) model does indeed exhibit better predictive capability, we also use the Holt–Winters model and a seasonal GM(1,1) model to predict monthly natural gas production, and compare the results with the model proposed here. The prediction results show that monthly natural gas production in China will continue to increase throughout the 2019–2023 period, that the peak-to-valley differences in monthly production values will also increase, and that the seasonal variations in production will become increasingly pronounced. Moreover, although Chinese production of natural gas is increasing, it will still be difficult to meet future demand, and hence the gap between supply and demand will increase year by year. We conclude that China needs to develop a more complete import plan for gas to meet expected natural gas consumption.

Suggested Citation

  • Li, Nu & Wang, Jianliang & Wu, Lifeng & Bentley, Yongmei, 2021. "Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization," Energy, Elsevier, vol. 215(PA).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220322258
    DOI: 10.1016/j.energy.2020.119118
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220322258
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119118?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    2. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    3. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    4. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    5. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    6. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    7. Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
    2. 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.
    3. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    4. Fan, Jingjing & Wang, Jianliang & Liu, Mingming & Sun, Wangmin & Lan, Zhixuan, 2022. "Scenario simulations of China's natural gas consumption under the dual-carbon target," Energy, Elsevier, vol. 252(C).
    5. Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
    6. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    7. Yang, Weixin & Pan, Lingying & Ding, Qinyi, 2023. "Dynamic analysis of natural gas substitution for crude oil: Scenario simulation and quantitative evaluation," Energy, Elsevier, vol. 282(C).
    8. Li, Zekai & Hu, Xi & Guo, Huan & Xiong, Xin, 2023. "A novel Weighted Average Weakening Buffer Operator based Fractional order accumulation Seasonal Grouping Grey Model for predicting the hydropower generation," Energy, Elsevier, vol. 277(C).
    9. 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).
    10. 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).
    11. 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.
    12. Oruc, Ridvan & Baklacioglu, Tolga, 2022. "Modeling of aircraft performance parameters with metaheuristic methods to achieve specific excess power contours using energy maneuverability method," Energy, Elsevier, vol. 259(C).
    13. Zhang, Kai & Yin, Kedong & Yang, Wendong, 2022. "Predicting bioenergy power generation structure using a newly developed grey compositional data model: A case study in China," Renewable Energy, Elsevier, vol. 198(C), pages 695-711.
    14. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    2. Luo, Xilin & Duan, Huiming & He, Leiyuhang, 2020. "A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy," Energy, Elsevier, vol. 205(C).
    3. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    5. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    6. Wang, Zheng-Xin & Li, Dan-Dan & Zheng, Hong-Hao, 2020. "Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    7. Bo Zeng & Shuliang Li & Wei Meng & Dehai Zhang, 2019. "An improved gray prediction model for China’s beef consumption forecasting," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-18, September.
    8. Wang, Zheng-Xin & He, Ling-Yang & Zheng, Hong-Hao, 2019. "Forecasting the residential solar energy consumption of the United States," Energy, Elsevier, vol. 178(C), pages 610-623.
    9. Zhou, Weijie & Wu, Xiaoli & Ding, Song & Pan, Jiao, 2020. "Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China," Energy, Elsevier, vol. 200(C).
    10. Wang, Xiaolei & Xie, Naiming & Yang, Lu, 2022. "A flexible grey Fourier model based on integral matching for forecasting seasonal PM2.5 time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    11. Chen, Lei & Huang, Ding-Bin & Wang, Shan-You & Nie, Yi-Nan & He, Ya-Ling & Tao, Wen-Quan, 2019. "A study on dynamic desorption process of methane in slits," Energy, Elsevier, vol. 175(C), pages 1174-1180.
    12. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    13. 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).
    14. Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
    15. Wen-Ze Wu & Chong Liu & Wanli Xie & Mark Goh & Tao Zhang, 2023. "Predictive analysis of the industrial water-waste-energy system using an optimised grey approach: A case study in China," Energy & Environment, , vol. 34(5), pages 1639-1656, August.
    16. Pawan Kumar Singh & Alok Kumar Pandey & S. C. Bose, 2023. "A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2429-2446, June.
    17. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    18. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    19. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
    20. Liu, Chong & Wu, Wen-Ze & Xie, Wanli & Zhang, Jun, 2020. "Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220322258. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.