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A hybrid BAG-SA optimal approach to estimate energy demand of China

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  • Wu, Qunli
  • Peng, Chenyang

Abstract

To improve projection efficiency of future energy demand, this paper proposes a hybrid optimization method, which is Bat Algorithm, Gaussian Perturbations and Simulated Annealing Energy Demand Estimation (BAG-SA EDE) model. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also enhances local search ability and speeds up the global convergence rate. The causality of energy demand and the selected factors is further investigated via the Stationarity, Cointegration and Granger causality tests. Then, BAG-SA algorithm is employed to optimize the coefficients of multiple linear and quadratic equations of energy demand estimation models. Results indicate that the proposed algorithm has higher precision and reliability than other single optimization methods, such as Genetic Algorithm, Particle Swarm Optimization or Bat Algorithm, and the quadratic form of BAG-SA EDE model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate energy demand of China from 2016 to 2030 in dissimilar scenarios. The forecasting findings show that energy demand in 2030 will be 4.6, 6.1 and 7.9 billion tce in three scenarios.

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  • Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:985-995
    DOI: 10.1016/j.energy.2016.12.002
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    2. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
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    4. Lin, Boqiang & Xu, Bin, 2018. "Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis," Energy, Elsevier, vol. 151(C), pages 167-177.
    5. Yuan, Xiao-Chen & Sun, Xun & Zhao, Weigang & Mi, Zhifu & Wang, Bing & Wei, Yi-Ming, 2017. "Forecasting China’s regional energy demand by 2030: A Bayesian approach," Resources, Conservation & Recycling, Elsevier, vol. 127(C), pages 85-95.

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