Applications of the Chaotic Quantum Genetic Algorithm with Support Vector Regression in 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, 2017, vol. 10, issue 11, 1-18

Abstract: 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.

Keywords: chaotic mapping function; support vector regression (SVR); quantum genetic algorithm (QGA); electricity demand 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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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