IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i19p8691-d1494553.html
   My bibliography  Save this article

Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning

Author

Listed:
  • Qingqing Wang

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Zhengshan Luo

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Pengfei Li

    (College of Economics and Management, Xi’an University of Posts and Telecommunications, Xi’an 710121, China)

Abstract

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.

Suggested Citation

  • Qingqing Wang & Zhengshan Luo & Pengfei Li, 2024. "Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning," Sustainability, MDPI, vol. 16(19), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8691-:d:1494553
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/19/8691/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/19/8691/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    2. Parikh, Jyoti & Purohit, Pallav & Maitra, Pallavi, 2007. "Demand projections of petroleum products and natural gas in India," Energy, Elsevier, vol. 32(10), pages 1825-1837.
    Full references (including those not matched with items on IDEAS)

    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. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    2. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    3. Raza, Muhammad Yousaf & Lin, Boqiang, 2023. "Future outlook and influencing factors analysis of natural gas consumption in Bangladesh: An economic and policy perspectives," Energy Policy, Elsevier, vol. 173(C).
    4. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).
    5. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
    6. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    7. Askari, S. & Montazerin, N. & Zarandi, M.H. Fazel, 2015. "Forecasting semi-dynamic response of natural gas networks to nodal gas consumptions using genetic fuzzy systems," Energy, Elsevier, vol. 83(C), pages 252-266.
    8. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    9. Sacchidananda Mukherjee, 2020. "Estimation and Projection of Petroleum Demand and Tax Collection from Petroleum Sector in India," Journal of Infrastructure Development, India Development Foundation, vol. 12(1), pages 39-68, June.
    10. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    11. García-Gusano, Diego & Suárez-Botero, Jasson & Dufour, Javier, 2018. "Long-term modelling and assessment of the energy-economy decoupling in Spain," Energy, Elsevier, vol. 151(C), pages 455-466.
    12. Wadud, Zia & Dey, Himadri S. & Kabir, Md. Ashfanoor & Khan, Shahidul I., 2011. "Modeling and forecasting natural gas demand in Bangladesh," Energy Policy, Elsevier, vol. 39(11), pages 7372-7380.
    13. Yusof, Ahmad & Raman, Maznah & Nopiah, Zulkifli, 2013. "Modeling of the Malaysian Crude Oil System," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 47(1), pages 125-130.
    14. Wang, Qiang & Li, Shuyu & Zhang, Min & Li, Rongrong, 2022. "Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach," Energy, Elsevier, vol. 239(PC).
    15. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Ethanol demand under the flex-fuel technology regime in Brazil," Energy Economics, Elsevier, vol. 33(6), pages 1146-1154.
    16. Xu Gong & Keqin Guan & Qiyang Chen, 2022. "The role of textual analysis in oil futures price forecasting based on machine learning approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1987-2017, October.
    17. Gielen, Dolf & Taylor, Peter, 2009. "Indicators for industrial energy efficiency in India," Energy, Elsevier, vol. 34(8), pages 962-969.
    18. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    19. Silva, Rodolfo Rodrigues Barrionuevo & Martins, André Christóvão Pio & Soler, Edilaine Martins & Baptista, Edméa Cássia & Balbo, Antonio Roberto & Nepomuceno, Leonardo, 2022. "Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems," Energy Economics, Elsevier, vol. 107(C).
    20. Weixin Sun & Heli Chen & Feng Liu & Yong Wang, 2025. "Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm," Annals of Operations Research, Springer, vol. 345(2), pages 1003-1033, February.

    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:gam:jsusta:v:16:y:2024:i:19:p:8691-:d:1494553. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.