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

Zhi Yuan,Yuan Yuan,Yi Liu,Zongzhi Li,Qian Zhang,Jingxian Liu

doi:10.1109/access.2020.3030614

Abstract

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 R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (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.

Highlights

The waterway transportation along the Yangtze River trunk line has effectively relieved the pressure on land transportation, railway transportation, and air transportation in China
To address the above issues, this article aims to develop a predictive model of inland ship consumption in terms of a complete future voyage comprehensively considering ship navigation status and environmental factors, and to implement a novel application of inland ship fuel consumption fitting analysis based on the developed model
WORK In this article, considering multi-source variables of ship navigation status and environmental factors, a novel application of inland ship fuel consumption fitting analysis was implemented based on developed DBPNN

Summary

Introduction

The waterway transportation along the Yangtze River trunk line has effectively relieved the pressure on land transportation, railway transportation, and air transportation in China. As people pay more attention to green shipping and ecological environment [1], energy management and resource optimization of inland waterway transportation have become an urgent problem to be solved [2]–[6]. Prediction of inland ship fuel consumption can provide a solid basis to solve these problems. For the past few years, some researchers have focused on ship fuel consumption prediction, and some achievements have been attained. Beşikçi et al [7] tried to predict ship fuel consumption for various operational conditions through using an ANN (artificial neural network). Coraddu et al [8] compared three different approaches WBM (White Box Model), BBM (Black Box Model) and GBM (Gray Box Model), in the prediction of the ship fuel consumption based on data measured by the on-board automation systems

Objectives

Results

Conclusion