Forecasting the Fuel Consumption of Passenger Ships with a Combination of Shallow and Deep Learning

Abstract

An accurate fuel consumption prediction system for transportation units is the pillar that a more efficient fuel management can rely on. This in turn may eventually lead to cost and emission savings for the unit’s owner. Numerous studies have been conducted for predicting the fuel usage in various means of transportation (i.e., airplanes, trucks, and vehicles). However, there is a limited number of researches that focus on passenger ships. These researches involve traditional machine learning models. There is a lack of literature on deep-learning-based forecasting models. The present paper serves as an initial study for exploring the potential of deep learning in day-ahead fuel consumption on a passenger ship. Firstly, a discussion is provided for the parameters that influence the fuel consumption. Secondly, the day-ahead fuel forecasting problem is formulated. To fully examine the influence of exogenous parameters on the consumption, various scenarios are formulated that differ in the types and number of inputs. The proposed forecasting model combines shallow and deep learning. Several machine learning and time series models were compared, and the results indicate the robustness of the proposed approach.

Predicting External Influences to Ship’s Average Fuel Consumption Based on Non-Uniform Time Set

“…We used all available data to obtain our results. Most of the references used a deep learning approach with ANNs [19][20][21][22][23]. On the other hand, it was not necessary to use ANNs always.…”

“…It is known in the literature [20][21][22] as the coefficient of determination that describes the proportion of dependent variable variance predictable from the independent variable. In addition, there is an adjusted R-square quality measure, which corrects the possible error in the R-square measure by increasing the number of samples, described as [27][28][29]:…”

“…To conclude, this section covers the mathematical background of the fitting functions, from (1) to (21) that will be used in the prediction. Equations from (22) to (28) describe the quality measures, which will be used to grade the fitting functions and determine which fitting function is best suited to predicting the average fuel consumption based on the non-uniform data time set. Finally, the theory of random variables is introduced in (29) to (36) that will be useful for identifying the optimal fitting function for prediction.…”

“…This research could be extended to a speculative approach-can the calculated consumption of the model be subtracted from the total consumption to obtain its environmental impact? Learning approaches to ship fuel consumption with ANNs are the most popular, and include [19][20][21][22][23]. On-line fuel consumption prediction was obtained by machine learning in [19].…”

“…On-line fuel consumption prediction was obtained by machine learning in [19]. Shalow and deep learning was combined in [22]. An outstanding result was the correlation matrix in [20], which correlated various causes of fuel consumption increase (e.g., wind, trim, currents, cargo, etc.).…”