Nowadays, there are more ships equipped with on-board monitoring systems and vessels with many sensors are becoming a standard. Available measurements can be employed for system optimization which play an important role in the vessel power plant configuration. The improvements in the power system design can be based on theoretical (modeling based on physical and empirical laws — for simulation purposes, for example [1]) or data-driven modeling (machine learning, statistical approach [2]). The data-driven models can be supportive confirming theoretical assumptions or simplifications from simulations. They are also helpful to understand the real systems, including vessel dynamic behavior and interactions. Therefore, the combination of simulation and data-driven modeling will be beneficial by identifying relationships that help explain unidentified variations. This approach is recommended when aiming for a more reliable tool for design optimization and to overcome the limitation of the simulation models that all system properties and dynamic effects must be known beforehand. The scope in this work is to present a potential synergy between the simulation and the machine learning approach. A data-driven method can be complementary to a model based on physical and empirical laws. This is shown in the example of power plant model connected with a thruster and vessel model to simulate the typical transit scenario and the data-driven model. The paper proposes a simultaneous analysis of the theoretical and machine learning models to predict the vessel power/speed and study the complex systems’ interactions in more detail, which are essential while exploring the system behavior.

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