[논문]Prediction Oil and Gas Throughput Using Deep Learning

Sangseop Lim

doi:10.9708/jksci.2023.28.05.155

Prediction Oil and Gas Throughput Using Deep Learning 원문보기

韓國컴퓨터情報學會論文誌 = Journal of the Korea Society of Computer and Information, v.28 no.5, 2023년, pp.155 - 161

Sangseop Lim (Div. of Navigation Convergence Studies, Korea Maritime and Ocean University)

초록
AI-Helper

우리나라 수출의 97.5%, 수입의 87.2%가 해상운송으로 이뤄지며 항만이 한국 경제의 중요한 구성요소이다. 이러한 항만의 효율적인 운영을 위해서는 항만 물동량의 단기 예측을 통해 개선시킬 수가 있으며 과학적인 연구방법이 필요하다. 이전 연구는 주로 장기예측을 기반으로 대규모 인프라투자를 위한 연구에 중점을 두었으며 컨테이너 항만물동량에만 집중한 측면이 크다. 본 연구는 국내 대표적인 석유항만인 울산항의 석유 및 가스화물 물동량에 대한 단기 예측을 수행하였으며 딥러닝 모델인 LSTM(Long Short Term Memory) 모델을 사용하여 RMSE기준으로 예측성능을 확인하였다. 본 연구의 결과는 석유 및 가스화물 물동량 수요 예측의 정확도를 높여 항만 운영의 효율성을 개선하는 근거가 될 수 있을 것으로 기대된다. 또한 기존 연구의 한계로 컨테이너 항만 물동량뿐만 아니라 석유 및 가스화물 물동량 예측에도 LSTM의 활용할 수 있다는 가능성을 확인할 수 있으며 향후 추가 연구를 통해 일반화가 가능할 것으로 기대된다.

Abstract ▼ AI-Helper

97.5% of our country's exports and 87.2% of imports are transported by sea, making ports an important component of the Korean economy. To efficiently operate these ports, it is necessary to improve the short-term prediction of port water volume through scientific research methods. Previous research has mainly focused on long-term prediction for large-scale infrastructure investment and has largely concentrated on container port water volume. In this study, short-term predictions for petroleum and liquefied gas cargo water volume were performed for Ulsan Port, one of the representative petroleum ports in Korea, and the prediction performance was confirmed using the deep learning model LSTM (Long Short Term Memory). The results of this study are expected to provide evidence for improving the efficiency of port operations by increasing the accuracy of demand predictions for petroleum and liquefied gas cargo water volume. Additionally, the possibility of using LSTM for predicting not only container port water volume but also petroleum and liquefied gas cargo water volume was confirmed, and it is expected to be applicable to future generalized studies through further research.

주제어

표/그림 (5)

그림 Fig. 1. Oil and Gas Throughput Volume of Ulsan
표 Table 1. Descriptive Statistics of Oil and Gas Throughput in Ulsan Port
그림 Fig. 2. Structure of Long and Short Term Memory
그림 Fig. 3. Prediction of Oil and Gas Throughput in Ulsan Port
표 Table 2. Performance of LSTM

AI 본문요약
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제안 방법

Therefore, future research should consider using data covering a more extended period. Overall, the study highlights several directions for future research to build on the findings of this study and improve the accuracy and generalizability of the forecasting models.

이론/모형

This study uses the Long Short Term Memory (LSTM) model, one of the deep learning models, to perform short-term forecasting on the petroleum throughput of the representative oil port in Korea, Ulsan Port. LSTM is widely used in natural language recognition fields such as speech and handwriting recognition, and is advantageous for learning due to the time series pattern of language, such as words and sentences.

성능/효과

kr) for monthly data on oil and gas cargo volume in Ulsan from January 2001 to December 2022. A total of 266 monthly time series data from January 2001 to December 2022 were used for analysis, and the training set and test set were divided at a ratio of about 8:2, with approximately 216 training data used from January 2001 to December 2018, and about 48 test data used from January 2019 to December 2022. Typically, for neural network models, the training set and test set are divided at a ratio of 7:3 or 8:2 for training.
Accurate short-term forecasts of throughput can help operators optimize their capacity allocation, which can lead to significant cost savings and improved operational efficiency. The accurate short-term forecasts of oil throughput can help port operators and stakeholders in the supply chain plan and manage their activities more effectively.
This study showed that the performance of LSTM model in predicting port demand can be scientifically utilized and is excellent in terms of prediction performance. In addition, short-term prediction can achieve port efficiency not only in container ports but also in oil and gas ports, demonstrating the distinctiveness of this study compared to other studies.
Therefore, future research should consider examining these ports to complement the research conducted in this study. It is important to note that the performance of the LSTM model in predicting short-term port demand may differ when applied to different ports. Thus, a reevaluation of the model's performance in predicting demand for other ports is necessary to verify its effectiveness.
Overall, demand forecasting is important prior to investing in seaport development because it can help with planning and design, ensuring that the port is built to meet the projected demand. It can also help with ensuring that the port has the necessary capacity to handle the demand, and can help with investment decisions by directing resources towards projects that are most likely to generate a return on investment.
The experimental results of short-term prediction in this paper show that the prediction performance of LSTM is quite significant, and it is expected to establish goals and plans for improving the efficiency of port operation through short-term prediction of oil and gas cargo volume.
To select the optimal model, we will apply the k-fold cross-validation method to set hyper-parameters and use RMSE as comparison criteria. The paper compares the final prediction results and demonstrate the superior short-term prediction performance of the LSTM model and its applicability to port cargo volume prediction.
This comparison will provide a better understanding of the capabilities of each model in predicting short-term port demand. The study acknowledges that the use of data solely from Ulsan port may limit the generalizability of the findings to other ports. Therefore, future research should include data from other ports to improve the generalizability of the findings.
The study acknowledges that there are other ports in Korea that handle oil and gas besides Ulsan, such as Yeosu and Daesan. Therefore, future research should consider examining these ports to complement the research conducted in this study.
This study showed that the performance of LSTM model in predicting port demand can be scientifically utilized and is excellent in terms of prediction performance. In addition, short-term prediction can achieve port efficiency not only in container ports but also in oil and gas ports, demonstrating the distinctiveness of this study compared to other studies.

후속연구

The previous studies reviewed so far have focused on short-term forecasting of container cargo volumes in seaports. In this study, we will attempt short-term forecasting of crude oil, petroleum products, and liquid gas cargo volumes at the Port of Ulsan.
Thus, a reevaluation of the model's performance in predicting demand for other ports is necessary to verify its effectiveness. Moreover, it is suggested that a comparison of the LSTM model with other forecasting models can be conducted to determine the strengths and weaknesses of each model. This comparison will provide a better understanding of the capabilities of each model in predicting short-term port demand.
The study acknowledges that there are other ports in Korea that handle oil and gas besides Ulsan, such as Yeosu and Daesan. Therefore, future research should consider examining these ports to complement the research conducted in this study. It is important to note that the performance of the LSTM model in predicting short-term port demand may differ when applied to different ports.
It is important to note that the data used in this study covers only a limited period, and the results may vary if the study is conducted using a longer timeframe. Therefore, future research should consider using data covering a more extended period. Overall, the study highlights several directions for future research to build on the findings of this study and improve the accuracy and generalizability of the forecasting models.
The study acknowledges that the use of data solely from Ulsan port may limit the generalizability of the findings to other ports. Therefore, future research should include data from other ports to improve the generalizability of the findings. It is important to note that the data used in this study covers only a limited period, and the results may vary if the study is conducted using a longer timeframe.
The structure of LSTM can also be applied to time series analysis, and its performance in time series forecasting is known to be superior to that of conventional artificial neural network or ARIMA models. Therefore, this study aims to achieve significant improvement in short-term forecasting through the application of LSTM to port throughput, and it is expected to contribute to the efficiency of port operations by increasing the accuracy of port throughput forecasting.
Moreover, it is suggested that a comparison of the LSTM model with other forecasting models can be conducted to determine the strengths and weaknesses of each model. This comparison will provide a better understanding of the capabilities of each model in predicting short-term port demand. The study acknowledges that the use of data solely from Ulsan port may limit the generalizability of the findings to other ports.
This study attempts to predict the demand for petroleum ports by predicting the volume of oil and gas cargo in the port of Ulsan through prediction analysis, and to contribute to the efficient operation of the port. By highlighting the importance of short-term prediction in port operation, this study will demonstrate its necessity and differentiation.
Thus, a reevaluation of the model's performance in predicting demand for other ports is necessary to verify its effectiveness

참고문헌 (13)

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상세보기
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상세보기
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상세보기
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원문보기 상세보기
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상세보기
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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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