IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v12y2021i4d10.1007_s13198-021-01116-9.html
   My bibliography  Save this article

Analysis and prediction of ship energy efficiency using 6G big data internet of things and artificial intelligence technology

Author

Listed:
  • Jianhua Deng

    (Shanghai CRRC Hange Marine & Offshore Engineering Co., Ltd)

  • Ji Zeng

    (Shanghai Maritime University)

  • Songyan Mai

    (Shanghai Maritime University)

  • Bowen Jin

    (Shanghai Maritime University)

  • Bo Yuan

    (Shanghai Maritime University)

  • Yunhui You

    (Shanghai Maritime University)

  • Shifeng Lu

    (Shanghai Maritime University)

  • Mengkai Yang

    (Shanghai Maritime University)

Abstract

The purpose is to solve the problem that the energy consumption on the ship in China has not been managed and monitored for a long time due to the lack of effective technical means. Analytic hierarchy process (AHP) is mainly used to establish a ship energy consumption evaluation index system. Indexes are selected and their weight are determined. Each index is analyzed in detail and modeling evaluation is carried out. In the aspect of energy consumption prediction, neural network combined with system identification theory is adopted, and the model is established according to the obtained data. Meanwhile, the short-term prediction of ship energy consumption is made. Based on the background of 6G communication technology, Internet of things and artificial intelligence technology, the method of combining C#.net interface development and MATLAB is adopted to design a set of ship energy consumption evaluation and prediction system, which can realize the ship energy consumption evaluation and prediction through direct parameter input. The performance of the ship energy consumption prediction system is good, the difference between the predicted value and the actual value is small, and the minimum relative error is only 0.017 %. The system can not only be used for energy consumption evaluation and prediction of ships, but also make the storage of ship information resources more convenient, which is more conducive to the establishment of knowledge base. It is of great significance to improve the comprehensive management ability of China’s shipping.

Suggested Citation

  • Jianhua Deng & Ji Zeng & Songyan Mai & Bowen Jin & Bo Yuan & Yunhui You & Shifeng Lu & Mengkai Yang, 2021. "Analysis and prediction of ship energy efficiency using 6G big data internet of things and artificial intelligence technology," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 824-834, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01116-9
    DOI: 10.1007/s13198-021-01116-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01116-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01116-9?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Mohsen Banaei & Fatemeh Ghanami & Mehdi Rafiei & Jalil Boudjadar & Mohammad-Hassan Khooban, 2020. "Energy Management of Hybrid Diesel/Battery Ships in Multidisciplinary Emission Policy Areas," Energies, MDPI, vol. 13(16), pages 1-16, August.
    2. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    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. Kelly Gerakoudi & Georgios Kokosalakis & Peter J. Stavroulakis, 2024. "A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry," Journal of Shipping and Trade, Springer, vol. 9(1), pages 1-29, December.

    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. Sekhar, Charan & Dahiya, Ratna, 2023. "Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand," Energy, Elsevier, vol. 268(C).
    2. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.
    4. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    5. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
    6. Ye-Rin Kim & Jae-Myeong Kim & Jae-Jung Jung & So-Yeon Kim & Jae-Hak Choi & Hyun-Goo Lee, 2021. "Comprehensive Design of DC Shipboard Power Systems for Pure Electric Propulsion Ship Based on Battery Energy Storage System," Energies, MDPI, vol. 14(17), pages 1-28, August.
    7. Lee, Junsoo & Kim, Tae Wan & Koo, Choongwan, 2022. "A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    8. Lin, Xiaojie & Zhang, Ning & Du-Ikonen, Liuliu & Yuan, Xiaolei & Zhong, Wei, 2025. "Investigation of building load prediction models based on integration of mechanism methods and data-driven models," Energy, Elsevier, vol. 324(C).
    9. Nivolianiti, Evaggelia & Karnavas, Yannis L. & Charpentier, Jean-Frederic, 2024. "Energy management of shipboard microgrids integrating energy storage systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    10. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    11. Shangfu Wei & Xiaoqing Bai, 2022. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network," Energies, MDPI, vol. 15(5), pages 1-21, February.
    12. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
    13. Zhang, Limao & Chen, Chao & Zhou, Cheng & Luo, Yongqiang & Wu, Xiaoying, 2025. "Zone-based many-objective building decarbonization considering outdoor temperature and occupation uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    14. Maciej Slowik & Wieslaw Urban, 2022. "Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant," Energies, MDPI, vol. 15(9), pages 1-16, May.
    15. Salari, Ali & Shakibi, Hamid & Soleimanzade, Mohammad Amin & Sadrzadeh, Mohtada & Hakkaki-Fard, Ali, 2024. "Application of machine learning in evaluating and optimizing the hydrogen production performance of a solar-based electrolyzer system," Renewable Energy, Elsevier, vol. 220(C).
    16. Hu, Yuqing & Cheng, Xiaoyuan & Wang, Suhang & Chen, Jianli & Zhao, Tianxiang & Dai, Enyan, 2022. "Times series forecasting for urban building energy consumption based on graph convolutional network," Applied Energy, Elsevier, vol. 307(C).
    17. Xu, Wen & Wu, Xianguo & Xiong, Shishu & Li, Tiejun & Liu, Yang, 2025. "Optimizing the sustainable performance of public buildings: A hybrid machine learning algorithm," Energy, Elsevier, vol. 320(C).
    18. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    19. Alharbi, Abdullah G. & Olabi, A.G. & Rezk, Hegazy & Fathy, Ahmed & Abdelkareem, Mohammad Ali, 2024. "Optimized energy management and control strategy of photovoltaic/PEM fuel cell/batteries/supercapacitors DC microgrid system," Energy, Elsevier, vol. 290(C).
    20. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01116-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.