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

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.

Automated summary

This study compared two methods, the simplified naval architecture method (SNAM) and the deep neural network (DNN) method, for predicting a ship's fuel consumption. The SNAM used limited operational data and a simplified technique to estimate the ship's required power by determining its resistance in calm water. The DNN method, trained on over 12,000 sample voyages, involved multiple fully connected layers and the ReLU activation function to predict fuel consumption based on realistic operational data.