Real-time optimization of ship energy efficiency based on the prediction technology of working condition

Ship energy efficiency management and control is an effective strategy to improve the marine economy and reduce CO2 emission. The determination of the best navigation speed under different working conditions is the basis and premise for real-time improvement of ship energy efficiency. In this paper, the working condition in short distance ahead of the ship related to navigation environment factors was predicted by the method of wavelet neural network, and then the best engine speed for the optimal energy efficiency under different working conditions could be determined through the established ship energy efficiency real-time optimization model. Further, by presetting the ship engine at this optimal speed, the ship energy efficiency could be guaranteed at the optimal state when the ship arrived at the navigation environment ahead of the ship, thus achieving real-time optimization of ship energy efficiency under different navigation environment factors. Experimental studies showed that the proposed optimization model was effective in energy saving and emission reduction, which could provide theoretical guidance for optimal sailing of the ship in service. Compared to traditional setting speed navigation methods, our proposed method has more practical significance to the improvement of ship energy efficiency.