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Forecasting of coal and electricity prices in China: Evidence from the quantum bee colony-support vector regression neural network

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  • Pan, Wenchao
  • Guo, Zhichen
  • Zhang, Jiayan Shi Yaxuan
  • Luo, Lingle

Abstract

Energy, the backbone of modern society, plays a crucial role in the development and productivity of a nation. Predictive analysis in energy management is becoming increasingly important. In addition, predicting the price fluctuations of the energy market can help energy companies formulate reasonable policies, reduce economic risks, and also provide a reference for the government to formulate energy policies.These optimized algorithms are then employed to optimize the Support Vector Regression (SVR) neural network further, aiming to enhance its prediction capability. The findings indicate that the quantum swarm model demonstrates the highest optimization level among the four models, emerging as the most effective tool for energy price prediction. The outcomes of this research can offer valuable insights for policymakers and investors in related fields, ultimately contributing to the stability and development of the energy market.

Suggested Citation

  • Pan, Wenchao & Guo, Zhichen & Zhang, Jiayan Shi Yaxuan & Luo, Lingle, 2024. "Forecasting of coal and electricity prices in China: Evidence from the quantum bee colony-support vector regression neural network," Energy Economics, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324002731
    DOI: 10.1016/j.eneco.2024.107565
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    References listed on IDEAS

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