Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

Ming-Wei Li, Jing Geng, Shumei Wang and Wei-Chiang Hong ()
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Ming-Wei Li: College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China
Jing Geng: College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China
Shumei Wang: School of Education Intelligent Technology, Jiangsu Normal University/101, Shanghai Rd., Tongshan District, Xuzhou 221116, Jiangsu, China

Energies, 2017, vol. 10, issue 12, 1-18

Abstract: Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.

Keywords: support vector regression; chaos theory; quantum behavior; bat algorithm (BA); 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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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