A Comparative Study to Estimate Fuel Consumption: A Simplified Physical Approach against a Data-Driven Model

Abstract

Two methods were compared to predict a ship’s fuel consumption: the simplified naval architecture method (SNAM) and the deep neural network (DNN) method. The SNAM relied on limited operational data and employed a simplified technique to estimate a ship’s required power by determining its resistance in calm water. Here, the Holtrop–Mennen technique obtained hydrostatic information for each selected voyage, the added resistance in the encountered natural seaways, and the brake power required for each scenario. Additional characteristics, such as efficiency factors, were derived from literature surveys and from assumed working hypotheses. The DNN method comprised multiple fully connected layers with the nonlinear activation function rectified linear unit (ReLU). This machine-learning-based method was trained on more than 12,000 sample voyages, and the tested data were validated against realistic operational data. Our results demonstrated that, for some ship topologies (general cargo and containerships), the physical models predicted more accurately the realistic data than the machine learning approach despite the lack of relevant operational parameters. Nevertheless, the DNN method was generally capable of yielding reasonably accurate predictions of fuel consumption for oil tankers, bulk carriers, and RoRo ships.

Power Prediction of a 15,000 TEU Containership: Deep-Learning Algorithm Compared to a Physical Model

“…The scope of this paper was to further develop the SNAM (Simplified Naval Architecture Method) of La Ferlita et al [15] by including additional parameters. Therefore, our present investigation considered a more accurate physical model.…”

“…Contrary to the assumptions applied above in the simplified method [15], here, the efficiency factors were directly calculated. For the transmission-shaft efficiency, a factor of 0.98 was considered as the machinery was positioned aft [23].…”

“…In our proposed neural network, one layer consisted of 8192 activation units and the other layer consisted of 4096 activation units. The network structure was similar to the one already proposed by La Ferlita et al [15], except for the input features. The same number of units for the first and second layers was considered, as in our previous study [15], because the amount of data available was similar and because the so-called curse of non-dimensionality [30] was taken into account.…”

“…The network structure was similar to the one already proposed by La Ferlita et al [15], except for the input features. The same number of units for the first and second layers was considered, as in our previous study [15], because the amount of data available was similar and because the so-called curse of non-dimensionality [30] was taken into account. A network with many layers and a limited amount of data is more likely to deliver poor predictions.…”

Coastal Air Quality Assessment through AIS-Based Vessel Emissions: A Daesan Port Case Study

“…Tarelko & Rudzki [19] and Tran [20] predicted ship fuel consumption using artificial neural network (ANN)-based algorithms targeting specific ship types. La Ferlita et al [21] compared fuel consumption predictions between a deep neural network (DNN) model, enhancing the learning capability of the ANN algorithm, and a simplified naval architecture method (SNAM) model. While the DNN model generally aligned well with actual fuel consumption, the SNAM method exhibited more favorable predictions for container ships and general cargo ships.…”