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Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model

Author

Listed:
  • Anna Manowska

    (Department of Electrical Engineering and Automation in Industry, Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Aurelia Rybak

    (Department of Electrical Engineering and Automation in Industry, Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Artur Dylong

    (Department of Electrical Engineering and Automation in Industry, Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, 44-100 Gliwice, Poland)

  • Joachim Pielot

    (Department of Electrical Engineering and Automation in Industry, Faculty of Mining, Safety Engineering and Industrial Automation, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

Natural gas is one of the main energy sources in Poland and accounts for about 15% of the primary energy consumed in the country. Poland covers only 1/5 of its demand from domestic deposits. The rest is imported from Russia, Germany, Norway, the Czech Republic, Ukraine, and Central Asia. An important issue concerning the market of energy resources is the question of the direct impact of the prices of energy resources on the income of exporting and importing countries. It should also be remembered that unexpected and large fluctuations are detrimental to both exporters and importers of primary fuels. The article analyzes natural gas deposits in Poland, raw material trade and proposes a model for forecasting the volume of its consumption, which takes into account historical consumption, prices of energy resources and assumptions of Poland’s energy policy until 2040. A hybrid model was built by combining ARIMA with LSTM artificial neural networks. The validity of the constructed model was assessed using ME, MAE, RMSE and MSE. The average percentage error is 2%, which means that the model largely represents the real gas consumption course. The obtained forecasts indicate an increase in consumption by 2040.

Suggested Citation

  • Anna Manowska & Aurelia Rybak & Artur Dylong & Joachim Pielot, 2021. "Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model," Energies, MDPI, vol. 14(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8597-:d:707029
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    References listed on IDEAS

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    1. Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
    2. Anna Bluszcz & Anna Manowska, 2021. "The Use of Hierarchical Agglomeration Methods in Assessing the Polish Energy Market," Energies, MDPI, vol. 14(13), pages 1-18, July.
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    Cited by:

    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Anna Manowska & Andrzej Nowrot, 2022. "Solar Farms as the Only Power Source for the Entire Country," Energies, MDPI, vol. 15(14), pages 1-15, July.
    3. Fang, Yu & Jia, Chunhong & Wang, Xin & Min, Fan, 2024. "A fusion gas load prediction model with three-way residual error amendment," Energy, Elsevier, vol. 294(C).
    4. Marta Sukiennik & Barbara Kowal, 2022. "Analysis and Verification of Space for New Businesses in Raw Material Market—A Case Study of Poland," Energies, MDPI, vol. 15(9), pages 1-17, April.

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