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Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States

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  • Wang, Qiang
  • Jiang, Feng

Abstract

Pennsylvania and Texas accounted for about 60% of U.S. total shale gas production. Better forecasting shale gas production in Pennsylvania and Texas can serve us to better predict U.S. shale gas production. In this work, we integrate the linear and nonlinear forecasting techniques in order to use the advantages and avoid the disadvantages of linear and nonlinear forecasting models, so as to improve forecasting accuracy. Specifically, we develop two hybrid forecasting techniques, i.e., nonlinear metabolic grey model–Autoregressive Integrated Moving Average Model (NMGM-ARIMA), and Autoregressive Integrated Moving Average Model - Artificial neural network (ARIMA-ANN). 60 samples (monthly shale gas production in Pennsylvania and Texas) are used to test these two proposed forecasting techniques and these existing single nonlinear (NMGM, and ANN) and linear (ARIMA) forecasting techniques. The results show that for samples from either Pennsylvania or Texas, the mean absolute percent error of NMGM-ARIMA (3.16%, 1.64%) is smaller than that of NMGM (4.31%, 2.98%) and ARIMA (3.53%, 2.03%), and that of ARIMA-ANN (2.06%, 1.38%) is also smaller than ARIMA (3.53%, 2.03%) and ANN (3.09%, 1.71%). The proposed hybrid NMGM-ARIMA and ARIMA-ANN can achieve more accurate forecasting effect than the single theory-based models that made them up, and can be used in forecasting other fuels. The forecasting results show growth rates of shale gas production in Pennsylvania is higher than Texas in 2017 and 2018.

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  • Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:781-803
    DOI: 10.1016/j.energy.2019.04.115
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    References listed on IDEAS

    as
    1. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    3. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    4. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    5. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    6. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2015. "Recurrent support vector regression for a non-linear ARMA model with applications to forecasting financial returns," Computational Statistics, Springer, vol. 30(3), pages 821-843, September.
    7. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    8. Robert W. Gilmer & Emily Kerr, 2010. "Natural gas from shale: Texas revolution goes global," Southwest Economy, Federal Reserve Bank of Dallas, issue Q3, pages 10-13.
    9. Semenychev, V.K. & Kurkin, E.I. & Semenychev, E.V., 2014. "Modelling and forecasting the trends of life cycle curves in the production of non-renewable resources," Energy, Elsevier, vol. 75(C), pages 244-251.
    10. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
    11. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    12. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    13. Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
    14. Nana Geng & Yong Zhang & Yixiang Sun & Yunjian Jiang & Dandan Chen, 2015. "Forecasting China’s Annual Biofuel Production Using an Improved Grey Model," Energies, MDPI, vol. 8(10), pages 1-20, October.
    15. Alsaleh, Mohd & Abdul-Rahim, A.S. & Mohd-Shahwahid, H.O., 2017. "Determinants of technical efficiency in the bioenergy industry in the EU28 region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1331-1349.
    16. Wang, Qiang & Chen, Xi & Jha, Awadhesh N. & Rogers, Howard, 2014. "Natural gas from shale formation – The evolution, evidences and challenges of shale gas revolution in United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 1-28.
    17. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    18. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    19. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    20. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
    21. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    22. Yasmine Rashed & Hilde Meersman & Eddy Van de Voorde & Thierry Vanelslander, 2017. "Short-term forecast of container throughout: An ARIMA-intervention model for the port of Antwerp," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(4), pages 749-764, December.
    23. Siami-Irdemoosa, Elnaz & Dindarloo, Saeid R., 2015. "Prediction of fuel consumption of mining dump trucks: A neural networks approach," Applied Energy, Elsevier, vol. 151(C), pages 77-84.
    24. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    25. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    26. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    27. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    28. Weijermars, Ruud, 2014. "US shale gas production outlook based on well roll-out rate scenarios," Applied Energy, Elsevier, vol. 124(C), pages 283-297.
    29. Alsaleh, Mohd & Abdul-Rahim, A.S., 2018. "Determinants of cost efficiency of bioenergy industry: Evidence from EU28 countries," Renewable Energy, Elsevier, vol. 127(C), pages 746-762.
    30. Mohd Alsaleh & Abdul Samad Abdul-Rahim, 2018. "The Economic Determinants of Bioenergy Trade Intensity in the EU-28: A Co-Integration Approach," Sustainability, MDPI, vol. 10(2), pages 1-20, February.
    31. Khatib, Hisham, 2014. "Oil and natural gas prospects: Middle East and North Africa," Energy Policy, Elsevier, vol. 64(C), pages 71-77.
    32. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2019. "Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique," Energy, Elsevier, vol. 169(C), pages 762-775.
    33. Wang, Qiang & Li, Rongrong, 2016. "Natural gas from shale formation: A research profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1-6.
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