## 1 Introduction
### Literature Review  
In the emerging era of green shipping, finding the golden balance between economic viability and environmental concerns is crucial for the maritime industry. Towards this path, the exploration of machine learning models has gained significant popularity in the sector in recent years. Numerous research studies have been conducted to investigate the use of machine learning in various aspects of the industry, with an overview of these studies provided in Table 1.  
One of the most popular research topics in this direction focuses on predicting the ship's power needs. In [6], the authors utilised three different machine learning methods--non-linear Principal Component Regression (NL-PCR), non-linear Partial Least Squares Regression (NL-PLSR) and probabilistic artificial neural networks (ANNs)--to estimate the hydrodynamic performance of a ship. This was carried out using in-service data from two sister ships, complemented by weather hind-cast data. In [7], several supervised machine learning algorithms, like XGBoost, ANN, and Support Vector Regression (SVR), were compared to develop a data-driven ship speed-power model using both ship operational parameters, collected from a worldwide sailing chemical tanker and PCTC vessel, along with met ocean environmental conditions encountered by the ships in their voyages. Similarly, in [8], the authors explored five machine learning algorithms--Multiple Linear Regression (MLR), Decision Tree (DT), K Nearest Neighbours (KNN), ANN and Random Forest (RF)--for predicting propulsion power, utilising operational features and weather conditions from five sister container ships (8700 TEU capacity) operating between Europe and South America. Furthermore, their model was also used to inform decisions regarding hull cleaning. In [9], the authors assessed the performance of SVR for the prediction of the propulsion power using sensor data from a single 200,000-ton bulk cargo ship, combined with wave height retrieved from the National Oceanic and Atmospheric Administration (NOAA) database over seven months, outperforming the ISO-15016 method. Similarly, in [10], an ANN was used to predict the power of the hull for optimising voyages and reducing carbon emissions, employing data from two car-carrying vessels for an eight-year period. Another study by [11] applied Linear Regression to predict shaft power, fuel consumption, and speed based on extensive data preparation and feature engineering. First, a shaft power prediction model was developed; subsequently, fuel consumption and speed prediction models were developed based on the shaft power prediction model. In [12], an ensemble neural network (ENN) and a single neural network (ANN) with two hidden layers were used to predict towboat shaft power. Furthermore, in [13], artificial neural networks were trained using data from similar ships to forecast power use, taking weather variables into account. Another study [8] evaluates five models--MRL, DT, KNN, ANN and RF--for shaft power prediction and highlights the importance of pre-processing. Environmental parameters, including wave statistics, are integrated into the analysis to enhance prediction accuracy, with the Random Forest (RF) model emerging as the most effective. In [7], the authors explored XGBoost, artificial neural network, support vector machine, and statistical regression methods for ship speed-power modelling. Deep learning models, such as the recurrent neural network (RNN) and convolutional neural network (CNN), have also been investigated in [14] for the prediction of the real-time power of turbines based on DCS data (recorded for 719 days), with recurrent neural networks (RNNs) identified as the most effective in balancing accuracy and efficiency for power prediction. The energy efficiency of a general cargo ship was researched by analysing its shaft power utilisation over a 16-month voyage [15].