Diversity, quality, and quantity of real ship data on the black-box and gray-box prediction models of ship fuel consumption

Establishing an efficient and reliable fuel consumption prediction model by using big data from ships facilitates optimized decision-making and ensures the green and intelligent development of ships. As data are crucial in model construction, this study presents a general multi-source data processing method for obtaining high-quality training data. A long short-term memory (LSTM) neural network, suitable for time-series data, was used to develop the fuel consumption black-box model. This was combined with the ship theory to establish a fuel consumption theoretical model, thereby generating the LSTM based gray-box model. The authors explored the impact of data diversity, quality, and quantity on black-box and gray-box models. Analysis of the navigation data of a passenger ship and meteorological data collected from the (ECMWF) and MeteoBlue indicated that the combination of variables obtained via the feature selection of the least absolute shrinkage and selection operator (LASSO) statistical method yielded the best overall prediction performance. Moreover, the gray-box model was relatively stable in terms of the changes in effective variables. An analysis of data quality revealed that the systematic processing of outliers, which improves the accuracy of both models by 6.19% compared with direct deletion. Furthermore, the gray-box models use less amounts of data than the black-box models to achieve higher accuracy.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2023.116434
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801823028184
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Cai, Zhiyuan
  • 0000-0002-7096-6658
  • Li, Lecheng
  • Yu, Long
  • 0000-0002-9177-7741
  • Li, Congbo
  • Sun, Miao
• Publication Date: 2024-1-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 116434
• Serial:
  • Ocean Engineering
  • Volume: 291
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Data management; Data quality; Fuel consumption; Passenger ships; Predictive models
• Identifier Terms: European Centre for Medium-Range Weather Forecasts
• Subject Areas: Data and Information Technology; Energy; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01904023
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 4 2024 10:47AM