Data driven natural gas spot price prediction models using machine learning methods

Su, Moting, Zhang, Zongyi, Zhu, Ye, Zha, Donglan and Wen, Wenying 2019, Data driven natural gas spot price prediction models using machine learning methods, Energies, vol. 12, no. 9, pp. 1-17, doi: 10.3390/en12091680.

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Title Data driven natural gas spot price prediction models using machine learning methods
Author(s) Su, Moting
Zhang, Zongyi
Zhu, Ye
Zha, Donglan
Wen, Wenying
Journal name Energies
Volume number 12
Issue number 9
Article ID 1680
Start page 1
End page 17
Total pages 17
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2019
ISSN 1996-1073
Keyword(s) natural gas price
natural gas price forecasting
prediction model
machine learning methods
Summary Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.
Language eng
DOI 10.3390/en12091680
Field of Research 09 Engineering
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, by the authors.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30121511

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