Electric Consumption Forecast for Ships Using Multivariate Bayesian Optimization-SE-CNN-LSTM

Abstract

Many studies on reducing greenhouse gas emissions from ships have been conducted to reduce environmental pollution. Reducing the fuel oil consumption of traditional and green ships is a key focus of these studies. The fuel oil consumption of the ship depends on electric loads. Thus, ship power load estimation is necessary to develop methods for reducing the fuel oil consumption of ships. However, data accessibility for ship power load estimation is low, limiting the number of relevant studies. This study proposes a model for estimating the actual power load of ships using squeeze and excitation (SE), a convolutional neural network (CNN), and long short-term memory (LSTM). The electric load, power generated by the generator, power consumption of the reefer container, rudder angle, water speed, wind speed, and wind angle of a ship were measured in 10-minute increments for approximately 145 d. The existing parallel and direct CNN-LSTM power load estimation models were used to evaluate the performance of the proposed model. The proposed model had the lowest root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), demonstrating the best ship power load estimation performance compared to existing power load estimation models.

Keywords

• ship power load
• convolutional neural network
• long short-term memory
• squeeze and excitation
• Bayesian optimization