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Forecasting the natural gas demand in China using a self-adapting intelligent grey model

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  • Zeng, Bo
  • Li, Chuan

Abstract

Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas.

Suggested Citation

  • Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:810-825
    DOI: 10.1016/j.energy.2016.06.090
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    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. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    3. Chang, Che-Jung & Li, Der-Chiang & Huang, Yi-Hsiang & Chen, Chien-Chih, 2015. "A novel gray forecasting model based on the box plot for small manufacturing data sets," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 400-408.
    4. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    5. Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
    6. Wu, Zhibin & Xu, Jiuping, 2013. "Predicting and optimization of energy consumption using system dynamics-fuzzy multiple objective programming in world heritage areas," Energy, Elsevier, vol. 49(C), pages 19-31.
    7. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    8. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    9. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    10. Yagang Zhang & Jingyun Yang & Kangcheng Wang & Yinding Wang, 2014. "Lorenz Wind Disturbance Model Based on Grey Generated Components," Energies, MDPI, vol. 7(11), pages 1-16, November.
    11. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    12. 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.
    13. An, Ning & Zhao, Weigang & Wang, Jianzhou & Shang, Duo & Zhao, Erdong, 2013. "Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting," Energy, Elsevier, vol. 49(C), pages 279-288.
    14. Xu, Weijun & Gu, Ren & Liu, Youzhu & Dai, Yongwu, 2015. "Forecasting energy consumption using a new GM–ARMA model based on HP filter: The case of Guangdong Province of China," Economic Modelling, Elsevier, vol. 45(C), pages 127-135.
    15. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
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