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Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

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  • Huanying Liu

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Yulin Liu

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Changhao Wang

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Yanling Song

    (College of Information and Engineering, Zhejiang Ocean University, Zhoushan 316022, China)

  • Wei Jiang

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Cuicui Li

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Shouxin Zhang

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China)

  • Bingyuan Hong

    (National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China
    National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

Abstract

China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China’s natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China’s natural gas demand in the next 10 years. The results show that the annual growth rate of China’s natural gas demand in the next 10 years will reach 13.33%, at 8.3 × 10 11 m 3 in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China’s natural gas demand from 2023 to 2033, and provides decision-making support for China’s energy structure adjustment and natural gas import trade.

Suggested Citation

  • Huanying Liu & Yulin Liu & Changhao Wang & Yanling Song & Wei Jiang & Cuicui Li & Shouxin Zhang & Bingyuan Hong, 2023. "Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China," Energies, MDPI, vol. 16(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4268-:d:1153534
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    References listed on IDEAS

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