Prediction and optimisation of fuel consumption for inland ships considering real-time status and environmental factors

The information about ships' fuel consumption is critical for condition monitoring, navigation planning, energy management and intelligent decision-making. Detailed analysis, modelling and optimisation of fuel consumption can provide great support for maritime management and operation and are of significance to water transportation. In this study, the real-time status monitoring data and hydrological data of inland ships are collected by multiple sensors, and a multi-source data processing method and a calculation method for real-time fuel consumption are proposed. Considering the influence of navigational status and environmental factors, including water depth, water speed, wind speed and wind angle, the Long Short-Term Memory (LSTM) neural network is then tailored and implemented to build models for prediction of real-time fuel consumption rate. The validation experiment shows the developed model performs better than some regression models and conventional Recurrent Neural Networks (RNNs). Finally, based on the fuel consumption rate model and the speed over ground model constructed by LSTM, the Reduced Space Searching Algorithm (RSSA) is successfully used to optimise the fuel consumption and the total cost of a whole voyage.