Prediction of Main Engine Speed and Fuel Consumption of Inland Ships Based on Deep Learning

Abstract

Abstract The accurate fuel consumption forecasting system is for shipping companies to carry out fuel management more effectively in order to improve economic benefits. This article aims to use deep learning algorithms to improve the accuracy of ship engine speed and fuel consumption prediction. First, the main factors affecting the engine speed and fuel consumption of inland watercrafts are analyzed, and the input parameters of the neural network model are determined. Secondly, based on the dynamic time series characteristics of inland water vessels, the LSTM (Long short-term memory) algorithm with “time parameters” was selected to establish a neural network prediction model. Finally, the difference between the LSTM deep neural network model and the traditional machine learning model is compared, and the obtained prediction data is compared with the actual data. The experimental results show the superiority of deep learning in the prediction of main engine speed and fuel consumption of inland watercraft.