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Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Author

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  • Guo-Feng Fan

    (Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China)

  • Shan Qing

    (Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China)

  • Hua Wang

    (Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China)

  • Wei-Chiang Hong

    (Department of Information Management, Oriental Institute of Technology/58 Sec. 2, Sichuan Rd., Panchiao, Taipei 220, Taiwan)

  • Hong-Juan Li

    (Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming University of Science and Technology, Kunming 650093, China)

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 a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

Suggested Citation

  • Guo-Feng Fan & Shan Qing & Hua Wang & Wei-Chiang Hong & Hong-Juan Li, 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:4:p:1887-1901:d:24702
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    References listed on IDEAS

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    Cited by:

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    2. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    3. Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
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    10. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    11. Radu Saulescu & Mircea Neagoe & Codruta Jaliu & Olimpiu Munteanu, 2021. "A Comparative Performance Analysis of Counter-Rotating Dual-Rotor Wind Turbines with Speed-Adding Increasers," Energies, MDPI, vol. 14(9), pages 1-21, May.
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