# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 1 Introduction
To keep the vessel in the calm to extremely high sea state, the DPS compensates its heading and position changes by using the thrusters, where the sea state changes effect on the thruster's power consumption [36]. Accordingly, the thruster's control speed is increased or decreased due to the high and low-level of thrusting respectively. Therefore, the pitch RPM set point in quick react provides a full load upper than the existing power capacity in the power system. In this condition, a proper thruster control command can be used to avoid a blackout. The DP system feeds by power and the sub-control system computes the information from the equivalent force, speed, and path directions for each thruster motor [37; 38]. With the purpose of compensating insufficient power demand, generators, shaft generators, and renewable energy storage are used in the majority of these modern vessels. Insufficient power possibly will give rise to lower thrust performance, position, and power failure [39; 40]. A key challenge for the DP vessel is that the crucial load, such as thrusters demand in rough to extreme sea state, might have an upper power consumption ramp rate than the capacity of the power generators and can potentially increase the risk of blackouts. To avoid losing the position due to power blackouts in high sea state, DPS has superiorities than other key loads that exist on the bus feeders. Typically, DPS reduces the thruster's power dramatically to avoid dynamic instability in power systems [41]. For example, the uncertainties in electrical power demand for the power management system (PMS) to supply power for DPS in maritime operations. In marine the vessels, the PMS is an advanced controller to define the optimum power flow according to DP power demand in the future operating condition. In the shipboard power plants, PMS is employed to control the generator, prevent power failure, constraints, load flow, and shedding. The DPS is given the existing power by PMS to compute corresponding commands force and direction for the thrust allocation system. Consequently, unknown DP load demand due to uncertainties of sea conditions in the future may be a problem for PMS to optimize power generators [42]. To provide high-quality energy for safe and economical entry into ships, ship owners face many technical and economic problems, such as the operational planning and control of the shipboard power systems. Optimization methods with the considerable cost-saving prospect are used for the optimal planning and operation of dynamic loads and modern drive systems [43]. To approach this target, predicting future thrust force is crucial requirements. It is therefore very important to predict the power consumption of the thrusters. The DP loading estimation period can be for one year or one month for long-term load forecasts (LOTLF), medium-term load forecasts (METLF), and one hour or a day ahead for the short-term load forecasts (SOTLF). Typically, the proposed forecasting methods are applied to determine production capability or power generation, distribution, program planning, and maintenance schedules, etc. [44; 45]. The SOTLF is essential to control and plan for the DP shipboard power system, which can be used as input for the power flow or fault analyses. The DP power demand forecasting can consider the impact of climate changes on the power system because of the characteristics of thruster consumers. The previous method for load forecasting performance by Fourier series or tendency curves regarding time functions. Moreover, an automatic regressive-moving average (ARIMA) method has been proposed to establish the load behavior [46]. Furthermore, they are used as an exponential smoothing model for METLF in the existence of power demand predictions. The authors in [47; 48] proposed a time variable, linear estimation models, such as automatic regression (AR) and an automatic variable for modeling uncertainty and load forecasting of consumer demand.