Development of a Fuel Consumption Prediction Model Based on Machine Learning Using Ship In-Service Data

As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operational data and be optimized for such an application. This paper presents models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models. These methods prevent overfitting and multicollinearity while providing practical applicability. To implement the prediction model, either an artificial neural network (ANN) or multiple linear regression (MLR) were applied, where the ANN-based models showed the best prediction accuracy for both variable selection methods. The goodness of fit of the models based on ANN ranged from 0.9709 to 0.9936. Furthermore, sensitivity analysis of the draught under normal operating conditions indicated an optimal draught of 14.79 m, which was very close to the design draught of the target ship, and provides the optimal fuel consumption efficiency. These models could provide valuable information for ship operators to support decision making to maintain efficient operating conditions.

Automated summary

This paper presents models for predicting fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with methods to select the proper input variables. Artificial neural network (ANN) and multiple linear regression (MLR) were applied to implement the prediction model, with ANN-based models showing the best prediction accuracy. Sensitivity analysis of draught under normal operating conditions indicated an optimal draught of 14.79 m, providing optimal fuel consumption efficiency.