Abstract
Due to the hike in fuel price and environmental awareness by the International Maritime Organization, more attention has been given in order to optimize the fuel consumption of ships. The capability to predict the fuel consumption of ships plays a significant role in the optimization process. To date, most research on predicting ship fuel consumption did not consider marine environmental factors such as wind, wave, current, and etc. Furthermore, traditional statistical methods on predicting ship fuel consumption have low accuracy. In this paper, two different sets of data showing the fuel consumption of a voyage ship with and without the influence of marine environmental factors were obtained. The Back-Propagation Neural Network (BPNN) and Gaussian Process Regression (GPR) techniques in machine learning were used to train and predict the two datasets. Thereafter, the predictive performance of these two techniques was compared and analyzed. Results showed that both techniques were able to accurately predict the ship fuel consumption, especially on the dataset with the influence of marine environmental factors. Quantitatively, the mean prediction accuracy for GPR (mean R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.9887) is slightly higher than BPNN (mean R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.9817). However, GPR requires longer runtime (mean T = 2236.4 s) compared to BPNN (mean T = 14.7 s). Due to the longer runtime, GPR is less preferable for online and real-time prediction of enroute ship fuel consumption. The ship real-time fuel consumption data can be accurately predicted by machine learning, which will be beneficial to achieve the goal of ship fuel consumption optimization and greenhouse gas emission reduction in the future.
Highlights
Ships are considered as the main way of cargo transportation in the world, and more than 90% of the world’s cargo are transported by ships [1]
The value of R2 is relatively larger in dataset a when compared to the value of R2 in dataset b. This observation indicates that the prediction accuracy of Back-Propagation Neural Network (BPNN) is higher when marine environmental factors are added into the dataset of ship fuel consumption
Ship fuel consumption is affected by many factors, which has caused difficulties to analyze using traditional statistical methods
Summary
Ships are considered as the main way of cargo transportation in the world, and more than 90% of the world’s cargo are transported by ships [1]. Statistical analysis [9], [10] uses data recorded during the operation of the ship such as unit fuel consumption, speed, draft, trim, etc, for the regression analysis and prediction. This approach is simple, but it has low the accuracy. The characteristics of ship fuel consumption data are obtained as follows: per hundred nautical miles fuel consumption (FCh), daily fuel consumption (FCd ), engine shaft (x1), ship speed (x2), average draft (x3), ship trim (x4), current speed (x5), current direction (x6), wind speed (x7), wind direction (x8), wave direction (x9) and wave height (x10). It is necessary to pre-process the ship fuel consumption data and this proved to be an important premise for the analysis of ship fuel consumption
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