Exploring Time-Series Deep Learning Models for Ship Fuel Consumption Prediction

Abstract

The fuel consumption of ships is an important component of shipping operation costs and also a significant source of greenhouse gas emissions. Accurate fuel consumption prediction is of great significance for optimizing the energy efficiency management of ships, reducing operating costs, and minimizing environmental pollution. In addition, we have also observed that the fuel consumption data of ships usually have a strong temporal correlation. Therefore, in order to study whether the time-series factors of ship fuel data are helpful for SFC prediction and the performance of various deep learning models in ship fuel consumption prediction, this paper proposes three classes of models for comparative study: RNN-based models, attention-based models such as Transformer and Informer, which are applied to the field of ship fuel consumption for the first time, and RNN–attention mixed models. The experimental results show that there is indeed a lag in ship navigation data, and the processing of time-series data is of great significance for fuel consumption prediction. Moreover, we have found that on real ship operation datasets, Informer is the best-performing model with 1.46 and 0.969 for MSE and R2 scores. The prediction performance of Informer is significantly better than that of other methods, which provides a new direction for future ship fuel consumption prediction.