Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network

• Published in: Energies (Basel) 2022-07, Vol.15 (13), p.4885
• Main Authors: Manowska, Anna, Bluszcz, Anna
• Format: Article
• Language: English
• Subjects: Alternative energy; Crude oil; crude oil consumption; crude oil trade; Energy; Energy industry; energy markets; Energy policy; Long term memory; LSTM; machine learning; Natural gas; Neural networks; Nonlinear phenomena; Oil; Raw materials; Recurrent neural networks
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15134885

Primary fuels, i.e., crude oil, natural gas, and power coal, dominate the total global demand for primary energy. Among them, crude oil plays a particularly important role due to the universality of applications and the practical lack of substitutes in transport. Crude oil is also one of the main sources of primary energy in Poland and accounts for around 30% of the energy consumed. Poland covers only 3% of its needs from domestic deposits. The rest is imported from Russia, Saudi Arabia, Nigeria, Great Britain, Kazakhstan, and Norway. Due to such a high import of raw material, Poland must anticipate future demand. On the one hand, this article aims to analyze the current (2020) and future (2040) crude oil consumption on the Polish market. The study analyzes the geopolitical and economic foundations of the functioning of the energy raw-materials market, the crude oil supply, the structure of Poland’s energy mix, and assumptions about the energy policy until 2040. On the other hand, conclusions from the research were used to build a model of crude oil consumption for the internal market. It has been also shown that the consumption of crude oil on the Polish market is a nonlinear phenomenon with a small set of statistical data, which makes it difficult to build an accurate model. This paper proposes a new model based on artificial neural networks that includes long-term memory (LSTM). The accuracy of the constructed model was assessed using the MSE, Theil, and Janus coefficients. The results show that LSTM models can be used to forecast crude oil consumption, and they cope with the nonstationary and nonlinear time series. Many important contemporary problems posed in the field of energy economy are also discussed, and it is proposed to solve them with the use of modern machine-learning tools.