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Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for 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

Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

Suggested Citation

  • Cheng-Wen Lee & Bing-Yi Lin, 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting," Energies, MDPI, vol. 9(11), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:873-:d:81422
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    References listed on IDEAS

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    Cited by:

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    7. Ricardo Vazquez & Hortensia Amaris & Monica Alonso & Gregorio Lopez & Jose Ignacio Moreno & Daniel Olmeda & Javier Coca, 2017. "Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project," Energies, MDPI, vol. 10(2), pages 1-23, February.
    8. Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.

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