## 1 Introduction  
With the gradually increasing penetration rate of new energy sources, the development of marine renewable energy has become a hot research topic at both home and abroad, where green solar power, as one of the solutions, will be of great significance for energy saving and emission reduction in the field of environmental transportation. In a land micro-grid, solar power generation is only affected by natural conditions, but shipborne solar systems, such as a mobile micro-grid, are not only affected by natural conditions, but also by the navigation and sway of the ship itself. Such a system's power generation characteristics are more complex and changeable, even when the ship is parked. Due to the sway of the hull, the output of photovoltaic power generation system is constantly changing. Similarly, rapid and random photovoltaic (PV) panel fluctuations add a non-ignorable difficulty to PV power forecasting and calculation [1; 2]. The power system in a ship featuring solar photovoltaic power generation is a complex system involving multiple variables and mutual coupling between various variables. It is necessary to fully consider the impact of ship motion and ship sway on solar output and accurately estimate the change of photovoltaic power, which has become a key problem for solar energy used on ships. According to the existing research, when calculating the shipborne photovoltaic output power, the current methods ignore the significant impact of ship movement on PV systems, typically calculating the output power of the photovoltaic system by assuming a fixed photovoltaic tilt [3; 4]. In the traditional mathematical model of a shipborne PV system, the inclination angle is not seen as a variable to calculate the output power. Research on the effect of a varying tilt angle toward photovoltaic output was carried out in [5; 6; 7; 8], where the change of PV inclination affected the control method based on MPPT (Maximum Power Point Tracking) and the power planning, scheduling, and management of the ship's grid. This variable is also a significant parameter to forecast short-term power fluctuations in order to achieve an accurate reference for proactive frequency control [3]. Additionally, power prediction methods based on tilt angle estimation are also relatively rare, and it is difficult to accurately predict the photovoltaic power when affected by multiple uncertainties, which is not conducive to calculating a series of stability control problems. Recently, machine learning has become an effective technology for solving nonlinear complex problems in systems. [PERSON] et al. focused on a short-term nonparametric probabilistic theory of solar power prediction which uses an extreme learning machine (ELM) [9]. A long short-term memory (LSTM) structure related to the forecasting problem for power was proposed in [10]. [PERSON] and [PERSON] et al. proposed an input delay neural network to analyze and forecast the feature of photovoltaic panel motion posture [11; 12; 13]. This approach had higher prediction accuracy and smaller prediction error. It is noticeable that the generalization ability of this structure is not strong enough, which means that the prediction effect is still unable to meet the actual engineering requirements. Additionally, refs. [14; 15; 16] features a study on using wavelet transform and support vector machine (SVM) algorithms to predict wind power. The above method is usually applied in the scenario of long-term power dispatching, where the methods are tested in a time span from 30 min to a few hours. However, for control operations with response levels in the second level, short-term accurate power prediction is necessary for a control strategy to achieve a better effect. The researchers in [3] used an ensemble-based ELM forecaster to predict power output, but the prediction time was too short and the response speed of the motor was relatively high, if the energy storage system is to work with the coordinated control of a ship's motor, the method mentioned in this article is insufficient.