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

Bo Hu, Jian Xu, Zuoxia Xing, Pengfei Zhang, Jia Cui and Jinglu Liu
Additional contact information
Bo Hu: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, 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

Energies, 2022, vol. 15, issue 8, 1-14

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.

Keywords: short-term park load forecasting; least squares support vector regression (LSSVR); complementary ensemble empirical mode decomposition (CEEMD); satin bower bird optimization algorithm (SBO); combination model; multiple linear 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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/8/2767/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/8/2767/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:8:p:2767-:d:790394

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().