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

Cheng-Wen Lee and Bing-Yi Lin
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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

Energies, 2016, vol. 9, issue 11, 1-16

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.

Keywords: support vector regression (SVR); quantum tabu search (QTS) algorithm; quantum computing mechanics; electric load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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