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Energies 2016, 9(3), 221; doi:10.3390/en9030221

Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting

1
College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China
2
Department of Industrial Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, New Taipei 220, Taiwan
3
School of Economics & Management, Nanjing Tech University, Nanjing 211800, China
4
Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, New Taipei 220, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Sukanta Basu
Received: 5 February 2016 / Revised: 9 March 2016 / Accepted: 16 March 2016 / Published: 19 March 2016
View Full-Text   |   Download PDF [4108 KB, uploaded 19 March 2016]   |  

Abstract

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD) method and quantum particle swarm optimization algorithm (QPSO) for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF) and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia) market and the New York Independent System Operator (NYISO, New York, USA) are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability. View Full-Text
Keywords: electric load forecasting; support vector regression; quantum theory; particle swarm optimization; differential empirical mode decomposition; auto regression electric load forecasting; support vector regression; quantum theory; particle swarm optimization; differential empirical mode decomposition; auto regression
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Peng, L.-L.; Fan, G.-F.; Huang, M.-L.; Hong, W.-C. Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting. Energies 2016, 9, 221.

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