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A hybrid procedure for energy demand forecasting in China

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  • Yu, Shi-wei
  • Zhu, Ke-jun

Abstract

Energy consumption in China is continuously increasing. Accordingly, the present paper aims to develop a hybrid procedure for energy demand forecasting in China with higher precision. The mechanism of the affecting factors of China’s energy demand is investigated via path-coefficient analysis. The main affecting factors include gross domestic product, population, economic structure, urbanization rate, and energy structure. These factors are the inputs of the model with three forms: linear, exponential, and quadratic. To obtain better parameters, an improved hybrid algorithm called PSO-GA (particle swarm optimization-genetic algorithm) is proposed. This proposed algorithm differs from previous hybrids in the two ways. First, the GA and PSO approaches produce a hybrid hierarchy. Second, two information transfers are accomplished in the process. Results of this study show that China’s energy demand will be 4.70 billion tons coal equivalent in 2015. Furthermore, the proposed forecast method shows its superiority compared with single optimization methods, such as GA, PSO or ant colony optimization, and multiple linear regressions.

Suggested Citation

  • Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
  • Handle: RePEc:eee:energy:v:37:y:2012:i:1:p:396-404
    DOI: 10.1016/j.energy.2011.11.015
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    4. Colmenar, J.M. & Hidalgo, J.I. & Salcedo-Sanz, S., 2018. "Automatic generation of models for energy demand estimation using Grammatical Evolution," Energy, Elsevier, vol. 164(C), pages 183-193.
    5. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
    6. Li, Bing-Bing & Liang, Qiao-Mei & Wang, Jin-Cheng, 2015. "A comparative study on prediction methods for China's medium- and long-term coal demand," Energy, Elsevier, vol. 93(P2), pages 1671-1683.
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    8. Yang, Qing & Zhang, Lei & Zou, Shaohui & Zhang, Jinsuo, 2020. "Intertemporal optimization of the coal production capacity in China in terms of uncertain demand, economy, environment, and energy security," Energy Policy, Elsevier, vol. 139(C).
    9. Xu Tang & Benjamin C. McLellan & Simon Snowden & Baosheng Zhang & Mikael Höök, 2015. "Dilemmas for China: Energy, Economy and Environment," Sustainability, MDPI, Open Access Journal, vol. 7(5), pages 1-13, May.
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    17. Xiao, Jin & Li, Yuxi & Xie, Ling & Liu, Dunhu & Huang, Jing, 2018. "A hybrid model based on selective ensemble for energy consumption forecasting in China," Energy, Elsevier, vol. 159(C), pages 534-546.
    18. Abdulkerim Karaaslan & Mesliha Gezen, 2017. "Forecasting of Turkey’s Sectoral Energy Demand by Using Fuzzy Grey Regression Model," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 67-77.
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    20. Pukšec, Tomislav & Mathiesen, Brian Vad & Novosel, Tomislav & Duić, Neven, 2014. "Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia," Energy, Elsevier, vol. 76(C), pages 198-209.
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