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

Author

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

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.

Suggested Citation

  • Wang, Kai & Hua, Yu & Huang, Lianzhong & Guo, Xin & Liu, Xing & Ma, Zhongmin & Ma, Ranqi & Jiang, Xiaoli, 2023. "A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023046
    DOI: 10.1016/j.energy.2023.128910
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    References listed on IDEAS

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    1. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    2. Chi, Hongtao & Pedrielli, Giulia & Ng, Szu Hui & Kister, Thomas & Bressan, Stéphane, 2018. "A framework for real-time monitoring of energy efficiency of marine vessels," Energy, Elsevier, vol. 145(C), pages 246-260.
    3. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    4. Wang, Kai & Xue, Yu & Xu, Hao & Huang, Lianzhong & Ma, Ranqi & Zhang, Peng & Jiang, Xiaoli & Yuan, Yupeng & Negenborn, Rudy R. & Sun, Peiting, 2022. "Joint energy consumption optimization method for wing-diesel engine-powered hybrid ships towards a more energy-efficient shipping," Energy, Elsevier, vol. 245(C).
    5. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    6. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
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    2. Wang, Qi & Suo, Ruixia & Han, Qiutong, 2024. "A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis," Energy, Elsevier, vol. 292(C).

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