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Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms

Author

Listed:
  • Wojciech Panek

    (Independent Expert, formerly Polish Natural Gas Distribution Operator-PSG Sp z o.o., Bandrowskiego 16, PL33100 Tarnów, Poland)

  • Tomasz Włodek

    (Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, Al. Mickiewicza 30, PL30059 Krakow, Poland)

Abstract

Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon.

Suggested Citation

  • Wojciech Panek & Tomasz Włodek, 2022. "Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms," Energies, MDPI, vol. 15(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:348-:d:717522
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    References listed on IDEAS

    as
    1. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    2. Piotr Kosowski & Katarzyna Kosowska, 2021. "Valuation of Energy Security for Natural Gas—European Example," Energies, MDPI, vol. 14(9), pages 1-19, May.
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    Cited by:

    1. Bilgili, Mehmet & Pinar, Engin, 2023. "Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye," Energy, Elsevier, vol. 284(C).
    2. Bartłomiej Gaweł & Andrzej Paliński, 2024. "Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series," Energies, MDPI, vol. 17(2), pages 1-25, January.

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