# Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management

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## Abstract

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## 1. Introduction

#### 1.1. Background

#### 1.2. Purpose

#### 1.3. Literature Review

## 2. Materials and Methods

#### 2.1. Data

#### 2.2. Research Modeling

#### 2.2.1. Simple RNN

#### 2.2.2. LSTM

#### 2.2.3. GRU

#### 2.2.4. Hyper Parameter Modelling

#### 2.3. Performance Indicators

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**LNG-capable orderbook trend (adapted from Clarkson research [5]). LNG, liquefied natural gas.

**Figure 2.**LNG bunker price trend in the port of Singapore (computed by the authors using data from Platts Bunkerwire [6]). LNG, liquefied natural gas; VLSFO, very low sulfur fuel oil.

**Figure 4.**Prediction of LNG bunker price by each method. RNN, recurrent neural network; LSTM, long short-term memory; GRU, gated recurrent unit.

Period | Prediction |
---|---|

Early 2020s | Gradual ramp up of deliveries of LNG-fueled ships |

2024–2030 | LNG-fueled ship deliveries begin to surpass those of conventional oil-fueled ships |

2030s | LNG-fueled ships shares begin to fall as zero-carbon technologies develop |

2040s | Zero-carbon vessels account for the major share of shipyard output |

Statistics | Weekly LNG Bunker Price | |
---|---|---|

Observations | 144 | |

Mean | 15.04 | |

Std. error | 0.83 | |

Median | 11.09 | |

Std. dev. | 10.00 | |

ADF test | t-stat. | −1.85 |

Prob | 0.353 |

Model | MAE | MSE | MAPE | RMSE | |
---|---|---|---|---|---|

Simple RNN | Tr | 1.17 | 6 | 11.81 | 2.45 |

Te | 4.26 | 38.13 | 14.14 | 6.18 | |

LSTM | Tr | 1.23 | 5.75 | 12.19 | 2.4 |

Te | 4.14 | 33.81 | 13.77 | 5.82 | |

GRU | Tr | 1.15 | 5.26 | 12.1 | 2.29 |

Te | 5.09 | 47.52 | 16.81 | 6.89 |

Benchmark | Squared Error | Absolute Error | Squared Proportional Error | |||
---|---|---|---|---|---|---|

Simple RNN | GRU | Simple RNN | GRU | Simple RNN | GRU | |

LSTM | −1.254 (0.105) | −2.623 (0.004) | −0.577 (0.282) | −3.159 (0.001) | −1.029 (0.152) | −3.289 (0.001) |

Simple RNN | −1.576 (0.058) | −2.160 (0.015) | −1.702 (0.044) |

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## Share and Cite

**MDPI and ACS Style**

Kim, K.; Lim, S.; Lee, C.-h.; Lee, W.-J.; Jeon, H.; Jung, J.; Jung, D.
Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management. *J. Mar. Sci. Eng.* **2022**, *10*, 1814.
https://doi.org/10.3390/jmse10121814

**AMA Style**

Kim K, Lim S, Lee C-h, Lee W-J, Jeon H, Jung J, Jung D.
Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management. *Journal of Marine Science and Engineering*. 2022; 10(12):1814.
https://doi.org/10.3390/jmse10121814

**Chicago/Turabian Style**

Kim, Kyunghwan, Sangseop Lim, Chang-hee Lee, Won-Ju Lee, Hyeonmin Jeon, Jinwon Jung, and Dongho Jung.
2022. "Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management" *Journal of Marine Science and Engineering* 10, no. 12: 1814.
https://doi.org/10.3390/jmse10121814