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

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  • Moting Su

    (School of Economics and Business Administration, Chongqing University, Chongqing 400030, China)

  • Zongyi Zhang

    (School of Economics and Business Administration, Chongqing University, Chongqing 400030, China)

  • Ye Zhu

    (School of Information Technology, Deakin University, Victoria 3125, Australia)

  • Donglan Zha

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Wenying Wen

    (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China)

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.

Suggested Citation

  • Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1680-:d:228064
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    as
    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Ji, Qiang & Zhang, Hai-Ying & Geng, Jiang-Bo, 2018. "What drives natural gas prices in the United States? – A directed acyclic graph approach," Energy Economics, Elsevier, vol. 69(C), pages 79-88.
    3. Ohana, Steve, 2010. "Modeling global and local dependence in a pair of commodity forward curves with an application to the US natural gas and heating oil markets," Energy Economics, Elsevier, vol. 32(2), pages 373-388, March.
    4. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    5. Lloyd, James Robert, 2014. "GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes," International Journal of Forecasting, Elsevier, vol. 30(2), pages 369-374.
    6. Hamid Abrishami & Vida Varahrami, 2011. "Different methods for gas price forecasting," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(96), pages 137-144, Diciembre.
    7. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    8. Afgan, Naim H. & Pilavachi, Petros A. & Carvalho, Maria G., 2007. "Multi-criteria evaluation of natural gas resources," Energy Policy, Elsevier, vol. 35(1), pages 704-713, January.
    9. Shi, Xunpeng & Variam, Hari M.P., 2017. "East Asia’s gas-market failure and distinctive economics—A case study of low oil prices," Applied Energy, Elsevier, vol. 195(C), pages 800-809.
    10. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    11. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    12. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2016. "The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspective," Energy, Elsevier, vol. 101(C), pages 266-277.
    13. Zhang, Wen Yu & Hong, Wei-Chiang & Dong, Yucheng & Tsai, Gary & Sung, Jing-Tian & Fan, Guo-feng, 2012. "Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting," Energy, Elsevier, vol. 45(1), pages 850-858.
    14. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
    15. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    16. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
    17. Xiangzheng Deng & Jianzhi Han & Fang Yin, 2012. "Net Energy, CO 2 Emission and Land-Based Cost-Benefit Analyses of Jatropha Biodiesel: A Case Study of the Panzhihua Region of Sichuan Province in China," Energies, MDPI, vol. 5(7), pages 1-15, June.
    18. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    Full references (including those not matched with items on IDEAS)

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