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A Novel ROA-Optimized CNN-BiGRU Hybrid Network with an Attention Mechanism for Ship Fuel Consumption Prediction

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Název: A Novel ROA-Optimized CNN-BiGRU Hybrid Network with an Attention Mechanism for Ship Fuel Consumption Prediction
Autoři: Zifei Wang, Kai Wang, Zhongwei Li, Hongzhi Liang, Shuo Yin, Qitai Ma, Diankang Zhang, Weijie Xiong
Zdroj: Journal of Marine Science and Engineering ; Volume 14 ; Issue 4 ; Pages: 324
Informace o vydavateli: Multidisciplinary Digital Publishing Institute
Rok vydání: 2026
Sbírka: MDPI Open Access Publishing
Témata: fuel consumption prediction, machine learning, swarm intelligence optimization algorithm, low-carbon shipping
Geografické téma: agris
Popis: Optimizing ship energy efficiency and advancing the green transition of the shipping industry depend on an accurate model for predicting ship fuel consumption (FC). This study builds a hybrid prediction model that combines a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism using operational data from ships. The model is tuned using the Red Kite Optimization Algorithm (ROA). First, correlations between ship navigational environmental data and operational data are analyzed, and cluster analysis is performed to select suitable input features. Subsequently, the ship FC prediction model based on ROA-CNN-BiGRU-Attention (RCGA) is developed. A case study shows that the RCGA model reaches a root mean square error (RMSE) as low as 0.0205 and an R2 value as high as 0.9330, demonstrating strong performance in dynamic shipping scenarios, with advantages in handling temporal dependencies and complex operational patterns. Moreover, the model exhibits reasonable robustness, providing some support for ship energy efficiency optimization and assisting the shipping industry in advancing low-carbon development and sustainable green transition.
Druh dokumentu: text
Popis souboru: application/pdf
Jazyk: English
Relation: https://dx.doi.org/10.3390/jmse14040324
DOI: 10.3390/jmse14040324
Dostupnost: https://doi.org/10.3390/jmse14040324
Rights: https://creativecommons.org/licenses/by/4.0/
Přístupové číslo: edsbas.A0C07007
Databáze: BASE
Popis
Abstrakt:Optimizing ship energy efficiency and advancing the green transition of the shipping industry depend on an accurate model for predicting ship fuel consumption (FC). This study builds a hybrid prediction model that combines a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism using operational data from ships. The model is tuned using the Red Kite Optimization Algorithm (ROA). First, correlations between ship navigational environmental data and operational data are analyzed, and cluster analysis is performed to select suitable input features. Subsequently, the ship FC prediction model based on ROA-CNN-BiGRU-Attention (RCGA) is developed. A case study shows that the RCGA model reaches a root mean square error (RMSE) as low as 0.0205 and an R2 value as high as 0.9330, demonstrating strong performance in dynamic shipping scenarios, with advantages in handling temporal dependencies and complex operational patterns. Moreover, the model exhibits reasonable robustness, providing some support for ship energy efficiency optimization and assisting the shipping industry in advancing low-carbon development and sustainable green transition.
DOI:10.3390/jmse14040324