  As the importance of eco-friendly transportation increases, providing an
efficient approach for marine vessel operation is essential. Methods for status
monitoring with consideration to the weather condition and forecasting with the
use of in-service data from ships requires accurate and complete models for
predicting the energy efficiency of a ship. The models need to effectively
process all the operational data in real-time. This paper presents models that
can predict fuel consumption using in-service data collected from a passenger
ship. Statistical and domain-knowledge methods were used to select the proper
input variables for the models. These methods prevent over-fitting, missing
data, and multicollinearity while providing practical applicability. Prediction
models that were investigated include multiple linear regression (MLR),
decision tree approach (DT), an artificial neural network (ANN), and ensemble
methods. The best predictive performance was from a model developed using the
XGboost technique which is a boosting ensemble approach. \rvv{Our code is
available on GitHub at
\url{https://github.com/pagand/model_optimze_vessel/tree/OE} for future
research.
