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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
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    References listed on IDEAS

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    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).
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