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Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO

Author

Listed:
  • Bo Hu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
    State Grid Liao Ning Electric Power Supply Co., Ltd., Shenyang 110004, China)

  • Jian Xu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Zuoxia Xing

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Pengfei Zhang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jia Cui

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jinglu Liu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

To improve the accuracy of park load forecasting, a combined forecasting method for short-term park load is proposed based on complementary ensemble empirical mode decomposition (CEEMD), sample entropy, the satin bower bird optimization algorithm (SBO), and the least squares support vector regression (LSSVR) model. Firstly, aiming at the random fluctuation of park load series, the modes with different characteristic scales are divided into low-frequency and high-frequency according to the calculation of sample entropy, which is based on the decomposition of historical park load data modes by CEEMD. The low-frequency is forecast by multiple linear regression (MLR), and the high-frequency component is the training input of the LSSVR forecasting model. Secondly, the SBO algorithm is adopted to optimize the regularization parameters and the kernel function width of LSSVR. Then, the park load forecasting model of each sequence component is built. The forecast output of each sequence component is superimposed to get the final park load forecast value. Finally, a case study of a park in Liaoning Province has been performed with the results proving that the proposed method significantly outperforms the state-of-art in reducing the difficulty and complexity of forecasting effectively, also eliminating the defect of large reconstruction error greatly through the decomposed original sequence by the ensemble empirical model.

Suggested Citation

  • Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2767-:d:790394
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    References listed on IDEAS

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    1. Chai, Shanglei & Li, Qiang & Abedin, Mohammad Zoynul & Lucey, Brian M., 2024. "Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives," Research in International Business and Finance, Elsevier, vol. 67(PA).

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