 Short-term natural gas consumption forecasting from long-term data collectionRadek Svoboda, Vojtech Kotik and Jan Platos Energy, 2021, vol. 218, issue C 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 However, the datasets used in these works could often not be published and used by other researchers.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. Keywords: Natural gas; Consumption; Forecasting; Demand; Big data; Machine learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:218:y:2021:i:c:s0360544220325378 DOI: 10.1016/j.energy.2020.119430 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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