A Hybrid WSO-Optimized AE-CNN-BiLSTM Model for Ship Fuel Consumption Prediction

In the context of the growing importance of accurate ship fuel consumption prediction for the shipping industry, this study delves into advanced prediction methods. Leveraging machine learning and deep learning techniques, it proposes a novel approach. The Whale Shark Optimization (WSO) algorithm is innovatively applied to optimize the hyper-parameters of a hybrid model, namely the AE - CNN - BiLSTM. This hybrid model combines the features of different components, with BiLSTM playing a crucial role in handling time - series data related to ship fuel consumption. By exploring the application of WSO in model tuning, the research not only enhances the performance of the model but also further validates the latent value of the hybrid model in accurately predicting ship fuel consumption. Experimental results demonstrate that this method significantly improves the accuracy and stability of ship fuel consumption prediction, providing valuable insights and practical solutions for the shipping industry to optimize fuel management and reduce operational costs.