# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 4 Test Results and Discussions
### Scenario 1: Operation at Sea State  
The performance of the proposed strategy to estimate average DP load patterns for the various sea condition for a month as shown in Figure 8a,b, the hourly thrusting load patterns during environmental disturbance from calm to high, such as peak load periods January 2020. We are observed that the deviations in the DP load demands for different days of the months. As shown in Figure 8b, the entire peaks load in the graph occurs in the rough to high sea conditions. Typically, the DP load demand is comparatively less on the calm-slight situation than the moderate weather condition. On calm situation days, DP load is lower than the other moderate, and rough conditions days. The results have obtained for four representative sea conditions. The weather conditions have classified as inputs, and output of the NARX model with the 168, 336, and 504 lead times as determined in Table 1 and Figure 6 for 31 days SOTLF based on sea conditions. The backpropagation learning constant and the momentum ratio in (14) have set to 0.75 and 0.1 as an initial guess, respectively. In Scenario 1, the regulating tolerance for the weights vector is selected as 0.005. The results are presented in Figure 10a,b and Table 3 in terms of the performance measurement indicators (PMIs) defined in (20) and (21). Accordingly, the average values of the _MAPE_ and \(\delta\) for LM-NARX-NN are about 0.08% and 0.13 MW, respectively. In the meantime, the maximum DP load demand error and percentage values belong to TDL-NN, which are 0.2 MW and 0.13%, respectively. In contrast, the NAR has a more accurate performance as shown in Table 3 with the \(\delta\) of 0.17 MW and _MAPE_ of 0.12% than those for the TDL-NN. However, as mentioned in Section 3, due to lack of external inputs the weather distortion cannot be considered during the offline network training. the delayed \(7\times 24\)-h DP load profile is used as an output, and the next three latest days for thrusting demand, and environmental distortion of 72 h for inputs data, therefore we applied the numbers of TDL 24, 48 and 72 in (15) and (16), respectively. When the NN training has executed, then the NARX model progresses with one day ahead to forecast the upcoming of the 24-h DP load by employing the previous 72-h sea state data and parameters as given in Tables 1 and 2. The total number of the input and hidden layers of neurons are adjusted as 7 and 25, respectively. To improve the weights vector fluctuations, one hidden layer is used with 25 different numbers of neurons and the weight vector convergence in a predefined tolerance. In this case, Table 4 has analyzed the average weekly DP load demand, which shows the changes in DP load profile through several days of the week. We have observed that the DP load profile has strong relation through weekdays, such as peak load periods in high sea state as shown in Figure 10(a). In some of the weekdays, the thrusting demand is lower than the other weekdays due to the Calm-Slight sea state. The error percentage of DP load in MW presents in Figure 10(b). The accuracy of the NARX and the minimum error percentage in Table 4 with other time-series forecasting techniques have used in the same previous network setting and evaluated. The results indicate the comparison of _MAPE_, error variance data of LM-NARX-BP with other time-series-NN model schemes. The minimum _MAPE_ and \(\delta\) have found nearly 0.12% and 0.12 MW, respectively by LM-NARX-NN. The maximum \(\delta\), and error percentage values correspond to TDL-NN, which are about 0.2 MW and 0.18%, respectively. One the other hand, the average values of PMIs in Table 4 show that for the NAR-NN it has a more accurate performance during the weekly prediction of DP demand in sea disturbances in a month rather than TDL-NN with \(\delta\) of 0.16 MW and _MAPE_ of 0.17%, respectively. Furthermore, the NAR-NN can predict the value of DP demand to approach approximately to that obtained from the NARX-NN.