[Paper Review] Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition

Yuwen Zhao, Baojun Hu+1 more|arXiv (Cornell University)|Sep 19, 2024

Hydrocarbon exploration and reservoir analysis6 citations

TL;DR

The paper uses a three-layer LSTM to forecast Brent crude oil prices in the near future using EIA spot data, addressing how low-carbon transition factors influence price dynamics.

ABSTRACT

In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon development in various countries, the dynamics of crude oil market have become more complicated and changeable. The price of crude oil is not only influenced by traditional factors such as supply and demand, geopolitical conflict and production technology, but also faces the challenges of energy policy transformation, carbon emission control and new energy technology development. This diversified driving factor makes the prediction of crude oil price not only very important in economic decision-making and energy planning, but also a key issue in financial markets.In this paper, the spot price data of European Brent crude oil provided by us energy information administration are selected, and a deep learning model with three layers of LSTM units is constructed to predict the crude oil price in the next few days. The results show that the LSTM model performs well in capturing the overall price trend, although there is some deviation during the period of sharp price fluctuation. The research in this paper not only verifies the applicability of LSTM model in energy market forecasting, but also provides data support for policy makers and investors when facing the uncertainty of crude oil price.

Motivation & Objective

• Motivate forecasting of Brent crude oil prices amid low-carbon shift and energy policy changes.
• Evaluate the applicability of LSTM neural networks for energy market forecasting.
• Provide data-driven insights to policymakers and investors under uncertainty in oil prices.

Proposed method

• Construct a three-layer LSTM neural network to predict Brent price for the next few days.
• Use European Brent crude oil spot price data from the U.S. Energy Information Administration as input.
• Assess the model's ability to capture overall price trends and deviations during sharp fluctuations.

Experimental results

Research questions

• RQ1 Can a three-layer LSTM accurately capture the overall trend of Brent crude oil prices?
• RQ2 How well does the LSTM forecast align with actual prices during periods of sharp price fluctuation?
• RQ3 What is the impact of low-carbon transition-related factors on the predictive performance for Brent prices?

Key findings

• The LSTM model captures the overall price trend for Brent crude oil.
• There is some deviation in forecasts during periods of sharp price fluctuation.
• The approach demonstrates applicability of LSTM in energy market forecasting and provides data support for policymakers and investors.