# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 1 Introduction
In [49], the author used the time-frequency technique known as an empirical decomposition for analyzing the short term power predicting based on the historical load data. The time series modeling is explained in terms of ARIMA and multiple analyses for SOTLF [50]. In the presence of a large data set, an artificial neural network (ANN)-based SOTLF model is used in [51; 52]. In [53; 54], an ANN-based Levenberg-Marquardt (LM) backpropagation (BP) algorithm for SOTLF and METLF is used. The METLF-based ANN model is discussed using a monthly historical data set [55]. A fuzzy based NN based BP algorithm with particle optimization method is used for METLF [56; 57]. Another combination load forecasting method which imposes in [58] as statistical and physical constraints. In [59], BP-based METLF analysis features such as complexity of parameter, operating speed, and convergence have been proposed. Moreover, the linear, statistical, dynamic, fuzzy logic and AI-based models are similarly used to predict wind speeds. Extensive analysis of wind speed and electric load prediction is investigated in literature [60] for LOTLF based on nonlinear recurrent neural networks (NRNN). In addition, the models are minimized by using gradient descent (GDC) algorithms to decrease the NN error signals locally. Furthermore, researchers in time series modeling techniques should determine the significance of the selected input parameter and their patterns in the data. Moreover, in [60], the authors are reviewed wind predicting methods by using the NN based automatic regression with recessive exogenous (ARX), automatic regression moving average with recessive exogenous (ARMAX), nonlinear automatic regression with recessive exogenous (NARX), and NN based LM-BP algorithms to regulate the weight of neurons for SOTLF of wind. In this paper, the concept of deep learning techniques is executed based NN-NARX algorithm as a recurrent network to predict the SOTLF in DPS for operational planning, and optimal dispatching between distributed generators. The precise DP demand forecasting and power estimation for compensating the vessel motion are considered. In this method to provide the necessary information about load changes for better decision-making of the PMS in sea disorders. Hence, the DP load profile is predicted by applying the autocorrelation methods as the climate-sensitive load, which is demonstrated by the proposed technique to overcome the difficulty of previous methods. With the aim of this, an LM-BP-NARX network with external inputs as hybrid modeling is proposed in this paper for SOTLF. The sea disturbances as exogenous inputs are considered to enhance the precision of DP load prediction. The suggested method is used for predicting the hourly, daily, and sea state pattern recognition for short-term thrusting power, that the LM-BP is employed to train the weights of NARX neurons collection. The performance of the proposed method is demonstrated and compared with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Furthermore, a literature review in [61] is presented for the comparison between the conventional and intelligence control methodologies for the DPS controller structure improvements in offshore ships. This paper is organized as follows. Section 2 reviews the artificial neural network. The proposed method is described in Section 3 to train, validate, and test of predicted DP load in different sea states. Section 4 shows the evaluation performance of the proposed method. Finally, Section 5 terminates a brief survey of the proposed work.