TY - JOUR

T1 - Modeling of Natural Gas Consumption Volumes in China Aided by Machine Learning Methods

AU - Filimonova, Irina V.

AU - Nemov, Vasily Yu

AU - Samatova, Anastasia P.

AU - Akopov, Nikita G.

N1 - The research was supported by a grant from the Russian Science Foundation No. 23-78-10157, https://rscf.ru/project/23-78-10157/. Modeling of Natural Gas Consumption Volumes in China Aided by Machine Learning Methods / I. V. Filimonova, V. Y. Nemov, A. P. Samatova, N. G. Akopov // Energy Systems Research. – 2024. – Vol. 7, No. 3(27). – P. 30-36. – DOI 10.25729/esr.2024.03.0004.

PY - 2024/11/25

Y1 - 2024/11/25

N2 - The paper applies machine learning methods to make projections of natural gas consumption volumes in China. It is critical for Russia as a major supplier of natural gas to China to have a reasonable estimate of possible volumes of exports. This contributes to the proper allocation of available raw materials, reduces the cost of excess gas storage, and also facilitates long-term planning for future trade. These aspects are critical for sustaining Russia's economic security and developing international economic relations. It is possible to estimate possible exports based on the projected volumes of natural gas consumption in China. This study uses machine learning methods, which are considered a promising data analysis tool, to model such consumption. We used multiple models for their benchmark comparison. Ridge regression was used as a linear model, whereas random forest and gradient boosting served as nonlinear models. The simulations performed proved gradient boosting to be the best choice. The study revealed the decisive role of socio-demographic factors, such as the population and the urban area size. The most significant factors were the total population, gas reserves, urban area size, number of passenger cars, and the population in urban agglomerations with over 1 million inhabitants. The most significant factors were the total population, gas reserves, urban area size, number of passenger cars, and the population in urban agglomerations with over 1 million inhabitants. a modification of the discounted cash flow method. The model is tested under the assumption that carbon regulation is carried out through the introduction of a carbon border tax.

AB - The paper applies machine learning methods to make projections of natural gas consumption volumes in China. It is critical for Russia as a major supplier of natural gas to China to have a reasonable estimate of possible volumes of exports. This contributes to the proper allocation of available raw materials, reduces the cost of excess gas storage, and also facilitates long-term planning for future trade. These aspects are critical for sustaining Russia's economic security and developing international economic relations. It is possible to estimate possible exports based on the projected volumes of natural gas consumption in China. This study uses machine learning methods, which are considered a promising data analysis tool, to model such consumption. We used multiple models for their benchmark comparison. Ridge regression was used as a linear model, whereas random forest and gradient boosting served as nonlinear models. The simulations performed proved gradient boosting to be the best choice. The study revealed the decisive role of socio-demographic factors, such as the population and the urban area size. The most significant factors were the total population, gas reserves, urban area size, number of passenger cars, and the population in urban agglomerations with over 1 million inhabitants. The most significant factors were the total population, gas reserves, urban area size, number of passenger cars, and the population in urban agglomerations with over 1 million inhabitants. a modification of the discounted cash flow method. The model is tested under the assumption that carbon regulation is carried out through the introduction of a carbon border tax.

KW - forecasting

KW - gradient boosting

KW - machine learning

KW - natural gas

KW - random forest

UR - https://www.mendeley.com/catalogue/eaa6a462-262b-3c46-8eb3-9c8cb92f82a2/

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-105008334987&origin=inward&txGid=4ff27d4b93b18509e34467dde240bc4c

UR - https://www.elibrary.ru/item.asp?id=75137226

U2 - 10.25729/esr.2024.03.0004

DO - 10.25729/esr.2024.03.0004

M3 - Article

VL - 7

SP - 30

EP - 36

JO - Energy Systems Research

JF - Energy Systems Research

SN - 2618-9992

IS - 3

ER -