• Energy Research

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads excluded various factors related to electric propulsion vessels and failed to reflect the high variability of loads. Therefore, this study aims to examine the efficiency of various multialgorithms regarding methods of forecasting electric propulsion vessel energy consumption from various data sampling frequencies. For this purpose, there are numerous machine learning algorithm sets based on convolutional neural network (CNN) and long short-term memory (LSTM) combination methods. The methodology developed in this study is expected to be utilized in training the optimal energy consumption forecasting model, which will support tracking of degraded performance in vessels, optimize transportation, reflect emissions accurately, and be applied ultimately as a basis for route optimization purposes.

The power load data of electric-powered ships vary with the ships’ operational status and external environmental factors such as sea conditions. Therefore, a model is required to accurately predict a ship’s power load, which depends on changes in the marine environment, weather environment, and the ship’s situation. This study used the power data of an actual ship to predict the power load of the ship. The research on forecasting a ship’s power load fluctuations has been quite limited, and the existing models have inherent limitations in predicting these fluctuations accurately. In this paper, A multiple feature extraction (MFE)-long short-term memory (LSTM) model with skip connections is introduced to address the limitations of existing deep learning models. This novel approach enables the analysis and forecasting of the intricate load variations in ships, thereby facilitating the prediction of complex load fluctuations. The performance of the model was compared with that of a previous convolutional neural network-LSTM network with a squeeze and excitation (SE) model and deep feed-forward (DFF) model. The metrics used for comparison were the mean absolute error, root mean squared error, mean absolute percentage error, and R-squared, wherein the best, average, and worst performances were evaluated for both models. The proposed model exhibited a superior predictive performance for the ship’s power load compared to that of existing models, as evidenced by the performance metrics: mean absolute error (MAE) of 55.52, root mean squared error of (RMSE) 125.62, mean absolute percentage error (MAPE) of 3.56, and R-squared (R2) of 0.86. Therefore, the proposed model is expected to be used for power load prediction during electric-powered ship operations.

The power load data of electric-powered ships vary with the ships’ operational status and external environmental factors such as sea conditions. Therefore, a model is required to accurately predict a ship’s power load, which depends on changes in the marine environment, weather environment, and the ship’s situation. This study used the power data of an actual ship to predict the power load of the ship. The research on forecasting a ship’s power load fluctuations has been quite limited, and the existing models have inherent limitations in predicting these fluctuations accurately. In this paper, A multiple feature extraction (MFE)-long short-term memory (LSTM) model with skip connections is introduced to address the limitations of existing deep learning models. This novel approach enables the analysis and forecasting of the intricate load variations in ships, thereby facilitating the prediction of complex load fluctuations. The performance of the model was compared with that of a previous convolutional neural network-LSTM network with a squeeze and excitation (SE) model and deep feed-forward (DFF) model. The metrics used for comparison were the mean absolute error, root mean squared error, mean absolute percentage error, and R-squared, wherein the best, average, and worst performances were evaluated for both models. The proposed model exhibited a superior predictive performance for the ship’s power load compared to that of existing models, as evidenced by the performance metrics: mean absolute error (MAE) of 55.52, root mean squared error of (RMSE) 125.62, mean absolute percentage error (MAPE) of 3.56, and R-squared (R2) of 0.86. Therefore, the proposed model is expected to be used for power load prediction during electric-powered ship operations.