# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 1 Introduction  
Operation in deep waters is one of the most challenging in marine industries due to the risk of maneuvering in sea conditions. To avoid using anchor chains, the speed and position of vessels must becounteracted to sea disorders such as waves, wind, and their directions continuously. For establishing the vessel location and headline automatically, vessels are equipped by the dynamic positioning system (DPS), propellers, thrusters [1]. The DPS was first used in the 1960s to control the movement of ships in three horizontal degrees of freedom (HDOF), including sway, surge, and yaw. Where proportional-integral-derivative (PID) controllers were applied in DPS with a low-pass filter control system. The PID control system changed the phase insufficiently, which caused the instability of the system [2]. Hence, [PERSON] et al. suggested Kalman filtering methods, randomized optimal control theory, and more progressive control techniques to improve the DPS efficiency on ships maneuvering. This was revealed in the theory of kinematic equations [3; 4; 5; 6; 7]. On the other hand, the results showed that the movement equations are linear at many predefined constant heading angles in HDOF and the stability of DPS couldn't be guaranteed. The study in [8] proposed the non-linear control technique based on the backstepping method, where sea disturbances are considered. Hence, the environmental disturbances affected by wind and waves cannot be neglected. Consequently, an inactive non-linear observer was recommended, which consists of an estimate for the low-frequency station bias to measure the speed of the ship during displacement and a wave filter to reduce the number of setting parameters [9]. The performance of the method for designing a proportional-differential (PD) with control law for evaluating the DPS output feedback is shown in [10]. In this evaluation with a ship model, the filter supervisor eliminates noises from the measurement of the position speed of the ship by applying a PD controller that changes gradually because of environmental variations. To decrease the deviation of vessels station from the set point while ecological disturbances cause unexpected sudden changes of position, an adaptive control technique is developed to approximate nonlinear DPS parameters with fuzzy logic theory or neural network (NN) in [11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. As a result, an adaptive NN based on a fuzzy inference system is proposed in [21; 22; 23], which encloses a fuzzy logic to adapt the NN weights for the period of learning procedure by using fuzzy rules. And so, the control parameters adjust automatically by applying the NN based fuzzy logic rules. In the proposed method, due to time-saving concerns, the mathematical model is not considered by the NN controller. To give emphasis to the necessity for further research, the study in [24; 25; 26; 27; 28; 29] presented a combined control method, and performance techniques in DP ships under slow to heavy sea conditions are presented, where the non-linear controller was used as an autonomous scale to control the switching to prevent instability and shutdown of the system. To increase control performance, model predictive control (MPC) is represented in [30; 31; 32; 33; 34; 35], which can be mathematically executed by a highly precise control method using linear models of the dynamic plant. Hence, MPC simulates the future movement of the ship by using previous control to predict appropriate control output responses, which performed constraints on both input and output variables to estimate the distortion effect on future procedure evolution. Therefore, the prediction model includes abilities in the optimization method to approximate the dynamic response of vessels over a specified time horizon. On the other hand, depending on the proposed controllers' nature, the control methods do not react quickly to sudden changes in environmental forces such as wind blows, unless the position predictor can recognize and take timely proper actions.