IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i22p7559-d677647.html
   My bibliography  Save this article

A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Author

Listed:
  • Lisha Li

    (CAAC Academy, Civil Aviation Flight University of China, Guanghan 618307, China)

  • Shuming Yuan

    (School of Management, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, China)

  • Yue Teng

    (China Eastern Technology Application Research and Development Center Co., LTD, Shanghai 201707, China)

  • Jing Shao

    (School of Management, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an 710072, China)

Abstract

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

Suggested Citation

  • Lisha Li & Shuming Yuan & Yue Teng & Jing Shao, 2021. "A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft," Energies, MDPI, vol. 14(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7559-:d:677647
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/22/7559/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/22/7559/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    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. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
    2. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    3. Lin, Jiang & Xu Liu, & Gang He,, 2020. "Regional electricity demand and economic transition in China," Utilities Policy, Elsevier, vol. 64(C).
    4. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    5. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    6. Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.
    7. Parajuli, Ranjan & Østergaard, Poul Alberg & Dalgaard, Tommy & Pokharel, Govind Raj, 2014. "Energy consumption projection of Nepal: An econometric approach," Renewable Energy, Elsevier, vol. 63(C), pages 432-444.
    8. Lena Ahmadi & Eric Croiset & Ali Elkamel & Peter L. Douglas & Woramon Unbangluang & Evgueniy Entchev, 2012. "Impact of PHEVs Penetration on Ontario’s Electricity Grid and Environmental Considerations," Energies, MDPI, vol. 5(12), pages 1-19, November.
    9. Collins, Ross D. & Selin, Noelle E. & de Weck, Olivier L. & Clark, William C., 2017. "Using inclusive wealth for policy evaluation: Application to electricity infrastructure planning in oil-exporting countries," Ecological Economics, Elsevier, vol. 133(C), pages 23-34.
    10. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    11. Cho, Youngsang & Lee, Jongsu & Kim, Tai-Yoo, 2007. "The impact of ICT investment and energy price on industrial electricity demand: Dynamic growth model approach," Energy Policy, Elsevier, vol. 35(9), pages 4730-4738, September.
    12. Varma, Rashmi & Sushil,, 2019. "Bridging the electricity demand and supply gap using dynamic modeling in the Indian context," Energy Policy, Elsevier, vol. 132(C), pages 515-535.
    13. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    14. Kostadin Yotov & Emil Hadzhikolev & Stanka Hadzhikoleva & Stoyan Cheresharov, 2022. "Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System," Sustainability, MDPI, vol. 14(17), pages 1-18, September.
    15. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    16. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    17. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    18. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
    19. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
    20. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.

    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:jeners:v:14:y:2021:i:22:p:7559-:d:677647. 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.