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Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

Yuan, Z, Liu, J, Liu, Y, Yuan, Y, Zhang, Q and Li, Z (2020) Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors. IEEE Access, 8. pp. 187441-187454. ISSN 2169-3536

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The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this article, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R2 (R-squared) increased to 0.9843. What’s more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences, 09 Engineering, 10 Technology
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HE Transportation and Communications
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 16 Nov 2020 11:48
Last Modified: 04 Sep 2021 06:23
DOI or ID number: 10.1109/access.2020.3030614
URI: https://researchonline.ljmu.ac.uk/id/eprint/14011
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