A Ship Energy Consumption Prediction Method Based on TGMA Model and Feature Selection
Yuhang Liu | Kai Wang | Yong Lu | Yongfeng Zhang | Zhongwei Li | Ranqi Ma | Lianzhong Huang
Optimizing ship energy efficiency is a crucial measure for reducing fuel use and emissions in the shipping industry. Accurate prediction models of ship energy consumption are essential for achieving this optimization. However, external factors affecting ship fuel consumption have not been comprehensively investigated, and many existing studies still face efficiency and accuracy challenges. In this study, we propose a neural network model called TCN-GRU-MHSA (TGMA), which incorporates the temporal convolutional network (TCN), the gated recurrent unit (GRU), and multi-head self-attention mechanisms to predict ship energy consumption. Firstly, the characteristics of ship operation data are analyzed, and appropriate input features are selected. Then, the prediction model is established and validated through application analysis. Using the proposed model, the prediction accuracy of ship energy consumption can reach up to 96.04%. Comparative analysis results show that the TGMA model outperforms existing models, including those based on LSTM, GRU, SVR, TCN-GRU, and BP neural networks, in terms of accuracy. Therefore, the developed model can effectively predict ship fuel usage under various conditions, making it essential for optimizing and improving ship energy efficiency.
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