IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v80y2023ics030142072200602x.html
   My bibliography  Save this article

Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM

Author

Listed:
  • Singh, Sanjeet
  • Bansal, Pooja
  • Hosen, Mosharrof
  • Bansal, Sanjeev K.

Abstract

•The current study examines the factors affecting Natural Gas Consumption and their role in the prediction of annual Natural Gas Consumption in the USA.•The study utilizes and compares two machine learning-based forecasting models - support vector machine (SVM) and artificial neural network (ANN) along with traditional multiple linear regression.•The annual data for the period from 1980 to 2020 is collected for the selected influence factors and NGC for prediction.•Based on the MAPE criterion the best model was chosen to be ANN with one nodal value and one hidden layer.•The predicted NGC values indicate a decreasing trend over the period 2021–2030.

Suggested Citation

  • Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
  • Handle: RePEc:eee:jrpoli:v:80:y:2023:i:c:s030142072200602x
    DOI: 10.1016/j.resourpol.2022.103159
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030142072200602X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.resourpol.2022.103159?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Öztunç Kaymak, Öznur & Kaymak, Yiğit, 2022. "Prediction of crude oil prices in COVID-19 outbreak using real data," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Liu, Ben-chieh, 1983. "Natural gas price elasticities : Variations by region and by sector in the USA," Energy Economics, Elsevier, vol. 5(3), pages 195-201, July.
    3. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    4. Luna, Marcos & Nicholas, Dominic, 2022. "An environmental justice analysis of distribution-level natural gas leaks in Massachusetts, USA," Energy Policy, Elsevier, vol. 162(C).
    5. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    6. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    7. Fang-Fang Li & Han Cao & Chun-Feng Hao & Jun Qiu, 2021. "Daily Streamflow Forecasting Based on Flow Pattern Recognition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4601-4620, October.
    8. Yubin Cai & Xin Ma & Wenqing Wu & Yanqiao Deng, 2021. "Forecasting Natural Gas Consumption in the US Power Sector by a Randomly Optimized Fractional Grey System Model," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, November.
    9. Uri, Noel D., 1989. "Natural gas demand by agriculture in the USA," Energy Economics, Elsevier, vol. 11(2), pages 137-146, April.
    10. Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
    11. Jonathan Kelley, 2017. "Human Gains and Losses from Global Warming: Satisfaction with the Climate in the USA, Winter and Summer, North and South," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 345-366, March.
    12. Rafindadi, Abdulkadir Abdulrashid & Ozturk, Ilhan, 2015. "Natural gas consumption and economic growth nexus: Is the 10th Malaysian plan attainable within the limits of its resource?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1221-1232.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Xiaobin & Sengupta, Tuhin & Si Mohammed, Kamel & Jamaani, Fouad, 2023. "Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods," Resources Policy, Elsevier, vol. 82(C).
    2. Donghuan Han & Wei Xiong & Tongwen Jiang & Shusheng Gao & Huaxun Liu & Liyou Ye & Wenqing Zhu & Weiguo An, 2023. "Investigation of the Water-Invasion Gas Efficiency in the Kela-2 Gas Field Using Multiple Experiments," Energies, MDPI, vol. 16(20), pages 1-22, October.
    3. Yousaf Raza, Muhammad & Lin, Boqiang, 2023. "Development trend of Pakistan's natural gas consumption: A sectorial decomposition analysis," Energy, Elsevier, vol. 278(PA).
    4. Merve Kayacı Çodur, 2023. "Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand," Energies, MDPI, vol. 17(1), pages 1-25, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan," Energy, Elsevier, vol. 219(C).
    2. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    3. Liu, Guixian & Dong, Xiucheng & Jiang, Qingzhe & Dong, Cong & Li, Jiaman, 2018. "Natural gas consumption of urban households in China and corresponding influencing factors," Energy Policy, Elsevier, vol. 122(C), pages 17-26.
    4. Salim Hamza Ringim & Abdulkareem Alhassan & Hasan Güngör & Festus Victor Bekun, 2022. "Economic Policy Uncertainty and Energy Prices: Empirical Evidence from Multivariate DCC-GARCH Models," Energies, MDPI, vol. 15(10), pages 1-18, May.
    5. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    6. Deng, Yanqiao & Ma, Xin & Zhang, Peng & Cai, Yubin, 2022. "Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization," Energy, Elsevier, vol. 260(C).
    7. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    8. Wei Sun & Chongchong Zhang, 2018. "A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting," Energies, MDPI, vol. 11(5), pages 1-18, May.
    9. Obsatar Sinaga & Mohd Haizam Mohd Saudi & Djoko Roespinoedji & Mohd Shahril Ahmad Razimi, 2019. "The Dynamic Relationship between Natural Gas and Economic Growth: Evidence from Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 388-394.
    10. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    11. Ren, Weijie & Li, Baisong & Han, Min, 2020. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    12. Jiawei Zhang & Lin Li & Qizhi Zhang & Yanbin Wu, 2022. "Optimization of Load Sharing in Compressor Station Based on Improved Salp Swarm Algorithm," Energies, MDPI, vol. 15(15), pages 1-18, August.
    13. Gautam, Tej K. & Paudel, Krishna P., 2018. "The demand for natural gas in the Northeastern United States," Energy, Elsevier, vol. 158(C), pages 890-898.
    14. Halim TATLI, 2022. "Long-term price and income elasticity of residential natural gas demand in Turkey," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(1(630), S), pages 101-122, Spring.
    15. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    16. Bakhsh, Satar & Zhang, Wei, 2023. "How does natural resource price volatility affect economic performance? A threshold effect of economic policy uncertainty," Resources Policy, Elsevier, vol. 82(C).
    17. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    18. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
    19. Xing Zhang, 2018. "Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm," Energies, MDPI, vol. 11(6), pages 1-18, June.
    20. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:80:y:2023:i:c:s030142072200602x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.