## 2 Methods and Materials
### Dataset Description
The _Wind Relative Direction_ depicts the direction from which the wind blows relative to the ship's direction, allowing us to evaluate wind-induced forces and their effects on navigation. The _Current Velocity_ denotes the speed of ocean currents, affecting both the ship's propulsion and the fuel consumption. The _Current Relative Direction_ refers to the direction of ocean currents relative to the ship's direction, which concerns drift and course corrections. The _Sea Temperature_ indicates the temperature of the water and allows us to convey its influence on the fouling effect. Finally, the _Sea Depth_ denotes the depth of the sea, which reflects potential challenges posed by deep waters. It should be noted that each data point is the average of real-time measured values from the vessel over one hour, accompanied by metadata such as information about the ship, the timestamp of when it was recorded and the ship's geolocation. These metadata are not used during the training, but they are useful in cross-checking our preprocessing steps. \begin{table}
\begin{tabular}{c l l} \hline \hline  & **Measured Feature** & **Unit of Measurement** \\ \hline \multirow{4}{*}{**Operational Conditions**} & GPS Speed & knots \\  & Draft & meters \\  & Days from Delivery & days \\  & Days from Dry Dock & days \\ \hline \multirow{4}{*}{**Environmental Conditions**} & Wave Height & meters \\  & Wave Relative Direction & degrees \\  & Wind Speed & knots \\ \cline{1-1}  & Wind Relative Direction & degrees \\ \cline{1-1}  & Current Velocity & meters per second \\ \cline{1-1}  & Current Relative Direction & degrees \\ \cline{1-1}  & Sea Temperature & Celsius \\ \cline{1-1}  & Sea Depth & meters \\ \hline \multicolumn{2}{c}{**Target Variable**} & Shaft Power & kilo watts \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The features of the dataset along with the units of measurement. The features are split into two categories: those that consider operational conditions and those that consider environmental ones. Draft reflects how deeply a vessel sits in the water and varies in direct relation to its loading status. As such, it serves as a practical and interpretable proxy for loading conditions in operational models. Additionally, all vessels in the dataset belong to a group of sister VLCCs with standardised engine configurations, which minimises inter-vessel variability due to differing propulsion systems. This consistency allows us to focus on operational and environmental drivers of shaft power without introducing additional confounding variables. To ensure that our models were trained with data points that solely represent normal operating conditions during a voyage, the first step was to identify entries where the ship may have been stationary or moving at very low speeds within a harbour, which would be less relevant for predicting shaft power in voyaging conditions. To achieve that, entries were excluded where the _GPS speed_ was below 5 knots. Furthermore, some outliers were noticed in the _Sea Temperature_ measurements with abnormal temperatures, like \(-5\)\({}^{\circ}\)C, so they were removed as well. The resulting dataset consists of approximately 168K entries of real operating and environmental conditions, providing a thorough understanding of the factors influencing ship performance and navigation, and enabling a comprehensive analysis of maritime operations, efficiency, and safety. Apart from the GPS speed and sea temperature filters described above, no other features contained extreme values requiring outlier removal. Furthermore, the dataset did not include any missing values, due to the controlled and systematic data acquisition by the GIS Vessels Monitoring Platform. As part of our preprocessing, normalisation was applied only for algorithms sensitive to input scales, namely k-Nearest Neighbors, SVM and neural networks.