Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting

Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang and Wei-Chiang Hong ()
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Li-Ling Peng: College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China
Guo-Feng Fan: College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China
Min-Liang Huang: Department of Industrial Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, New Taipei 220, Taiwan

Energies, 2016, vol. 9, issue 3, 1-20

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.

Keywords: electric load forecasting; support vector regression; quantum theory; particle swarm optimization; differential empirical mode decomposition; auto regression (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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