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Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

Author

Listed:
  • Ming-Wei Li

    (College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China)

  • Jing Geng

    (College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China)

  • Shumei Wang

    (School of Education Intelligent Technology, Jiangsu Normal University/101, Shanghai Rd., Tongshan District, Xuzhou 221116, Jiangsu, China)

  • Wei-Chiang Hong

    (School of Education Intelligent Technology, Jiangsu Normal University/101, Shanghai Rd., Tongshan District, Xuzhou 221116, Jiangsu, China)

Abstract

Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.

Suggested Citation

  • Ming-Wei Li & Jing Geng & Shumei Wang & Wei-Chiang Hong, 2017. "Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting," Energies, MDPI, vol. 10(12), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2180-:d:123542
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    References listed on IDEAS

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    Cited by:

    1. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    2. Ming-Wei Li & Jing Geng & Wei-Chiang Hong & Yang Zhang, 2018. "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting," Energies, MDPI, vol. 11(9), pages 1-22, August.
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    5. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.

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