TITLE

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

AUTHOR(S)
Cheng-Wen Lee; Bing-Yi Lin
PUB. DATE
November 2016
SOURCE
Energies (19961073);Nov2016, Vol. 9 Issue 11, p873
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
119725092

 

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