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Energies 2016, 9(11), 873; doi:10.3390/en9110873

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

1
Department of International Business, Chung Yuan Christian University/200 Chung Pei Rd., Chungli District, Taoyuan City 32023, Taiwan
2
Ph.D. Program in Business, College of Business, Chung Yuan Christian University/200 Chung Pei Rd., Chungli District, Taoyuan City 32023, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Wei-Chiang Hong
Received: 22 July 2016 / Revised: 9 October 2016 / Accepted: 10 October 2016 / Published: 26 October 2016
View Full-Text   |   Download PDF [1706 KB, uploaded 26 October 2016]   |  

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. View Full-Text
Keywords: support vector regression (SVR); quantum tabu search (QTS) algorithm; quantum computing mechanics; electric load forecasting support vector regression (SVR); quantum tabu search (QTS) algorithm; quantum computing mechanics; electric load forecasting
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Lee, C.-W.; Lin, B.-Y. Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting. Energies 2016, 9, 873.

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