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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhenxing Zhao & Kaijie Chen & Ying Chen & Yuxing Dai & Zeng Liu & Kuiyin Zhao & Huan Wang & Zishun Peng, 2021. "An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing," Energies, MDPI, vol. 14(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5752-:d:634298
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    References listed on IDEAS

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    4. Anagnostos, D. & Schmidt, T. & Cavadias, S. & Soudris, D. & Poortmans, J. & Catthoor, F., 2019. "A method for detailed, short-term energy yield forecasting of photovoltaic installations," Renewable Energy, Elsevier, vol. 130(C), pages 122-129.
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    6. Zhao, Pan & Wang, Peizi & Xu, Wenpan & Zhang, Shiqiang & Wang, Jiangfeng & Dai, Yiping, 2021. "The survey of the combined heat and compressed air energy storage (CH-CAES) system with dual power levels turbomachinery configuration for wind power peak shaving based spectral analysis," Energy, Elsevier, vol. 215(PB).
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    Cited by:

    1. Yashuo Li & Bo Zhao & Weipeng Zhang & Liguo Wei & Liming Zhou, 2022. "Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning," Agriculture, MDPI, vol. 12(12), pages 1-17, December.
    2. Jose Cruz & Christian Romero & Oscar Vera & Saul Huaquipaco & Norman Beltran & Wilson Mamani, 2023. "Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Lev," Energies, MDPI, vol. 16(12), pages 1-21, June.

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