期刊
ENERGY
卷 218, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119430
关键词
Natural gas; Consumption; Forecasting; Demand; Big data; Machine learning
资金
- European Regional Development Fund in A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration project within the Operational Programme Research, Development and Education [CZ.02.1.01/0.0/0.0/17_049/0008425]
The development of natural gas consumption forecasting tools is crucial for forecasting models, with research efforts focused on creating a new dataset with relevant data features. A forecasting methodology is proposed to evaluate statistical and machine learning algorithms in the time series forecasting domain, along with the availability of the new dataset for research use.
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 However, the datasets used in these works could often not be published and used by other researchers.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. (C) 2020 Elsevier Ltd. All rights reserved.
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