Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

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ID: 42576
2019
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Abstract
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.
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su2019dataenergies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Su, Moting;Zhang, Zongyi;Zhu, Ye;Zha, Donglan;Wen, Wenying;
Journal energies
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/1996-1073/12/9/1680
Keywords Keywords not found

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