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Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting

Author

Listed:
  • 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)

  • Wei-Chiang Hong

    (School of Economics & Management, Nanjing Tech University, Nanjing 211800, China
    Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, New Taipei 220, Taiwan)

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.

Suggested Citation

  • Li-Ling Peng & Guo-Feng Fan & Min-Liang Huang & Wei-Chiang Hong, 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting," Energies, MDPI, vol. 9(3), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:221-:d:66131
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    References listed on IDEAS

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    1. Afshar, K. & Bigdeli, N., 2011. "Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)," Energy, Elsevier, vol. 36(5), pages 2620-2627.
    2. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    3. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    4. Peng, Huaiwu & Liu, Fangrui & Yang, Xiaofeng, 2013. "A hybrid strategy of short term wind power prediction," Renewable Energy, Elsevier, vol. 50(C), pages 590-595.
    5. Bernard, Jean-Thomas & Bolduc, Denis & Yameogo, Nadège-Désirée, 2011. "A pseudo-panel data model of household electricity demand," Resource and Energy Economics, Elsevier, vol. 33(1), pages 315-325, January.
    6. Che, Jinxing & Wang, Jianzhou & Wang, Guangfu, 2012. "An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting," Energy, Elsevier, vol. 37(1), pages 657-664.
    7. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2015. "Long term outlook of primary energy consumption of the Italian thermoelectric sector: Impact of fuel and carbon prices," Energy, Elsevier, vol. 87(C), pages 153-164.
    8. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    9. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
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    Cited by:

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