An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing

Zhenxing Zhao, Kaijie Chen, Ying Chen, Yuxing Dai, Zeng Liu, Kuiyin Zhao, Huan Wang and Zishun Peng
Additional contact information
Zhenxing Zhao: School of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411100, China
Kaijie Chen: School of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411100, China
Ying Chen: Growatt New Energy Technology (Thailand) Co., Ltd., Shenzhen 518000, China
Yuxing Dai: School of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China
Zeng Liu: School of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411100, China
Kuiyin Zhao: School of Electrical and Information Engineering, Hunan Institute of Engineering, Xiangtan 411100, China
Huan Wang: School of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China
Zishun Peng: School of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China

Energies, 2021, vol. 14, issue 18, 1-15

Abstract: With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.

Keywords: advanced smoothing control strategy; hybrid local search; power fluctuation in seconds; single-iteration (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/18/5752/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/18/5752/ (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:14:y:2021:i:18:p:5752-:d:634298

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 ().