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Energies 2016, 9(6), 426; doi:10.3390/en9060426

Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting

Department of Industrial Management, Oriental Institute of Technology/58 Sec. 2, Sichuan Rd, Panchiao, New Taipei 220, Taiwan
Academic Editor: Sukanta Basu
Received: 25 February 2016 / Revised: 18 May 2016 / Accepted: 24 May 2016 / Published: 31 May 2016
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Abstract

In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (PSO) algorithms, trapping in local optima results in an earlier standstill of the particles and lost activities, thus, its core drawback is that eventually it produces low forecasting accuracy. To continue exploring possible improvements of the PSO algorithm, such as expanding the search space, this paper applies quantum mechanics to empower each particle to possess quantum behavior, to enlarge its search space, then, to improve the forecasting accuracy. This investigation presents a support vector regression (SVR)-based load forecasting model which hybridizes the chaotic mapping function and quantum particle swarm optimization algorithm with a support vector regression model, namely the SVRCQPSO (support vector regression with chaotic quantum particle swarm optimization) model, to achieve more accurate forecasting performance. Experimental results indicate that the proposed SVRCQPSO model achieves more accurate forecasting results than other alternatives. View Full-Text
Keywords: support vector regression (SVR); chaotic quantum particle swarm optimization (CQPSO); quantum behavior; electric load forecasting support vector regression (SVR); chaotic quantum particle swarm optimization (CQPSO); quantum behavior; electric load forecasting
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Huang, M.-L. Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting. Energies 2016, 9, 426.

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