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Short-term natural gas consumption forecasting from long-term data collection

Author

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  • Svoboda, Radek
  • Kotik, Vojtech
  • Platos, Jan

Abstract

The development of natural gas consumption forecasting tools is an important application of forecasting models. Plenty of research efforts have already been made in this area. However, the datasets used in these works could often not be published and used by other researchers. This complicates further research and the comparison of forecasting methods. In this work, we address this issue by the creation of a new dataset. We have taken into account state-of-the-art research works and included many data features that were previously proven to have a significant impact on the precision of the model. A forecasting methodology suitable for the evaluation of statistical and machine learning algorithms used in the time series forecasting domain is proposed to validate the high usability of the new dataset. The results of the application of the methodology and their discussion are included. Moreover, we made this dataset available for everyone to use for their research purposes.

Suggested Citation

  • Svoboda, Radek & Kotik, Vojtech & Platos, Jan, 2021. "Short-term natural gas consumption forecasting from long-term data collection," Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220325378
    DOI: 10.1016/j.energy.2020.119430
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    References listed on IDEAS

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    3. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
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    5. Liu, Jinyuan & Wang, Shouxi & Wei, Nan & Qiao, Weibiao & Li, Ze & Zeng, Fanhua, 2023. "A clustering-based feature enhancement method for short-term natural gas consumption forecasting," Energy, Elsevier, vol. 278(PB).
    6. Xiong, Pingping & Li, Kailing & Shu, Hui & Wang, Junjie, 2021. "Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model," Energy, Elsevier, vol. 237(C).
    7. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.

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