This page has only limited features, please log in for full access.
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.
Cheng-Wen Lee; Bing-Yi Lin. Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting. Energies 2017, 10, 1832 .
AMA StyleCheng-Wen Lee, Bing-Yi Lin. Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting. Energies. 2017; 10 (11):1832.
Chicago/Turabian StyleCheng-Wen Lee; Bing-Yi Lin. 2017. "Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in Load Forecasting." Energies 10, no. 11: 1832.
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.
Cheng-Wen Lee; Bing-Yi Lin. Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting. Energies 2016, 9, 873 .
AMA StyleCheng-Wen Lee, Bing-Yi Lin. Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting. Energies. 2016; 9 (11):873.
Chicago/Turabian StyleCheng-Wen Lee; Bing-Yi Lin. 2016. "Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting." Energies 9, no. 11: 873.