A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis

Qi Wang, Ruixia Suo and Qiutong Han

Energy, 2024, vol. 292, issue C

Abstract: Natural gas, as a clean and low-carbon energy resource, assumes a vital role in facilitating the transformation of the Chinese energy structure. Effectively forecasting its consumption holds great practical implications for the high-quality social development of China. Therefore, a hybrid model for forecasting natural gas consumption (NGC) in China is developed in this paper. Firstly, the Logarithmic Mean Divisia Index (LMDI) method is adopted to decompose the influencing factors of the NGC in China from 1994 to 2020, which revealed that the energy structure effect and the economic development effect have a positive promotion on NGC, while the energy intensity effect manifests a significant inhibition. Subsequently, based on the contribution rate of each factor, the particle swarm optimization (PSO) algorithm to optimize the long and short-term memory neural network (LSTM) model is constructed for NGC forecasting. Compared to other benchmark models, the PSO-LSTM model demonstrated a significant improvement in predictive accuracy, showcasing its valuable application in NGC prediction. Finally, the PSO-LSTM model is employed to analyze the scenario prediction of NGC development from 2021 to 2035. The forecast result indicated that the future NGC in China will show a yearly growing trend, which may lead to a serious imbalance between the supply and demand of natural gas, so the Chinese government should fully consider the energy security issue when formulating relevant policies.

Keywords: Natural gas consumption; LMDI decomposition; PSO-LSTM model; Scenario analysis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224002068
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002068

DOI: 10.1016/j.energy.2024.130435

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().