A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data

Kai Wang, Yu Hua, Lianzhong Huang, Xin Guo, Xing Liu, Zhongmin Ma, Ranqi Ma and Xiaoli Jiang

Energy, 2023, vol. 282, issue C

Abstract: Optimization of ship energy efficiency is an efficient measure to decrease fuel usage and emissions in the shipping industry. The accurate prediction model of ship energy usage is the basis to achieve optimization of ship energy efficiency. This study investigates the sequential properties of the actual voyage data from a VLOC. On this basis, a model for predicting ship energy consumption is established by adopting a LSTM neural network that has better prediction performance for sequential datasets. To further enhance the performance of the established LSTM-based model, the network structures and hyperparameters are optimized by using Genetic Algorithm. Lastly, the application analysis is conducted to validate the established GA-LSTM-based model for ship fuel usage prediction. The established model for ship energy usage shows a significant improvement in prediction accuracy, compared to the original LSTM-based model. Meanwhile, the developed prediction model is more accurate than the existing BP, SVR, and ARIMA-based energy consumption models. The prediction errors for the ship's operational energy efficiency adopting the established GA-LSTM-based model can reach as low as 0.29%. Therefore, the established model can effectively predict the ship fuel usage under different conditions, which is essential for the optimization and improvement of ship energy efficiency.

Keywords: Ship energy efficiency; LSTM neural network; Genetic algorithm; Energy consumption prediction; Shipping decarbonization (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023046

DOI: 10.1016/j.energy.2023.128910

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