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Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network

Author

Listed:
  • Bingchun Liu

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China)

  • Chuanchuan Fu

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China)

  • Arlene Bielefield

    (Department of Information and Library Science, Southern Connecticut State University, New Haven, CT 06514, USA)

  • Yan Quan Liu

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
    Department of Information and Library Science, Southern Connecticut State University, New Haven, CT 06514, USA)

Abstract
The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic variables; the gross domestic product (GDP), population, imports, and exports. The performance of various forecasting models was assessed via MAPE and RMSE, and three scenarios were configured based on different sources of variable data. In predicting Chinese energy consumption from 2015 to 2021, results from the established GRU model of the highest predictive accuracy showed that Chinese energy consumption would be likely to fluctuate from 2954.04 Mtoe to 5618.67 Mtoe in 2021.

Suggested Citation

  • Bingchun Liu & Chuanchuan Fu & Arlene Bielefield & Yan Quan Liu, 2017. "Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network," Energies, MDPI, vol. 10(10), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1453-:d:112757
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    References listed on IDEAS

    as
    1. ToksarI, M. Duran, 2009. "Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey," Energy Policy, Elsevier, vol. 37(3), pages 1181-1187, March.
    2. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    3. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    4. Ratnakar Pani & Ujjaini Mukhopadhyay, 2010. "Identifying the major players behind increasing global carbon dioxide emissions: a decomposition analysis," Environment Systems and Decisions, Springer, vol. 30(2), pages 183-205, June.
    5. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    6. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    7. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    8. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    9. O'Neill, Brian C. & Desai, Mausami, 2005. "Accuracy of past projections of US energy consumption," Energy Policy, Elsevier, vol. 33(8), pages 979-993, May.
    10. 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.
    11. Wang, Yanjia & Gu, Alun & Zhang, Aling, 2011. "Recent development of energy supply and demand in China, and energy sector prospects through 2030," Energy Policy, Elsevier, vol. 39(11), pages 6745-6759.
    12. Liu, Xiuli & Moreno, Blanca & García, Ana Salomé, 2016. "A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors," Energy, Elsevier, vol. 115(P1), pages 1042-1054.
    13. Torrini, Fabiano Castro & Souza, Reinaldo Castro & Cyrino Oliveira, Fernando Luiz & Moreira Pessanha, Jose Francisco, 2016. "Long term electricity consumption forecast in Brazil: A fuzzy logic approach," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 18-27.
    14. Bakhat, Mohcine & Rosselló, Jaume, 2011. "Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain," Energy Economics, Elsevier, vol. 33(3), pages 437-444, May.
    15. Iniyan, S. & Suganthi, L. & Samuel, Anand A., 2006. "Energy models for commercial energy prediction and substitution of renewable energy sources," Energy Policy, Elsevier, vol. 34(17), pages 2640-2653, November.
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