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

TL;DR

This paper develops a three-layer LSTM model to predict Brent crude oil prices amidst complex low-carbon transition factors, demonstrating its effectiveness in capturing overall trends and aiding decision-making.

Contribution

It introduces a deep learning approach using a three-layer LSTM for crude oil price prediction under low-carbon transition influences, expanding forecasting methods in energy markets.

Findings

LSTM model effectively captures overall price trends.

Model shows some deviations during sharp fluctuations.

Provides data support for policymakers and investors.

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.