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Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting

Author

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  • Cheng-Wen Lee

    (Department of International Business, Chung Yuan Christian University, 200 Chung Pei Rd., Chungli District, Taoyuan City 32023, Taiwan)

  • Bing-Yi Lin

    (Ph.D. Program in Business, College of Business, Chung Yuan Christian University, 200 Chung Pei Rd., Chungli District, Taoyuan City 32023, Taiwan)

Abstract

Accurate electricity forecasting is still the critical issue in many energy management fields. The applications of hybrid novel algorithms with support vector regression (SVR) models to overcome the premature convergence problem and improve forecasting accuracy levels also deserve to be widely explored. This paper applies chaotic function and quantum computing concepts to address the embedded drawbacks including crossover and mutation operations of genetic algorithms. Then, this paper proposes a novel electricity load forecasting model by hybridizing chaotic function and quantum computing with GA in an SVR model (named SVRCQGA) to achieve more satisfactory forecasting accuracy levels. Experimental examples demonstrate that the proposed SVRCQGA model is superior to other competitive models.

Suggested Citation

  • Cheng-Wen Lee & Bing-Yi Lin, 2017. "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting," Energies, MDPI, vol. 10(11), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1832-:d:118421
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    References listed on IDEAS

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    Cited by:

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    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, August.
    3. Yujing Liu & Ruoyun Du & Dongxiao Niu, 2022. "Forecast of Coal Demand in Shanxi Province Based on GA—LSSVM under Multiple Scenarios," Energies, MDPI, vol. 15(17), pages 1-16, September.
    4. Cheng Yan & Jianfeng Zhu & Xiuli Shen & Jun Fan & Dong Mi & Zhengming Qian, 2020. "Ensemble of Regression-Type and Interpolation-Type Metamodels," Energies, MDPI, vol. 13(3), pages 1-20, February.
    5. 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.
    6. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    7. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.

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