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Photovoltaic Power Prediction Based on VMD-BRNN-TSP

Author

Listed:
  • Guici Chen

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Tingting Zhang

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Wenyu Qu

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Wenbo Wang

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

Abstract

Overfitting often occurs in neural network training, and neural networks with higher generalization ability are less prone to this phenomenon. Aiming at the problem that the generalization ability of photovoltaic (PV) power prediction model is insufficient, a PV power time-sharing prediction (TSP) model combining variational mode decomposition (VMD) and Bayesian regularization neural network (BRNN) is proposed. Firstly, the meteorological sequences related to the output power are selected by mutual information (MI) analysis. Secondly, VMD processing is performed on the filtered sequences, which is aimed at reducing the non-stationarity of the data; then, normalized cross-correlation (NCC) and signal-to-noise ratio (SNR) between the components obtained by signal decomposition and the original data are calculated, after which the key influencing factors are screened out to eliminate the correlation and redundancy of the data. Finally, the filtered meteorological sequences are divided into two datasets based on whether the irradiance of the day is zero or not. Meanwhile, the predictions are performed using BRNN for each of the two datasets. Then, the results are reordered in chronological order, and the prediction of PV power is realized conclusively. It was experimentally verified that the mean absolute value error ( M A E ) of the method proposed in this paper is 0.1281 , which is reduced by 40.28 % compared with the back propagation neural network (BPNN) model on the same dataset, the mean squared error ( M S E ) is 0.0962 , and the coefficient of determination ( R 2 ) is 0.9907 . Other error indicators also confirm that VMD is of much significance and TSP is contributive.

Suggested Citation

  • Guici Chen & Tingting Zhang & Wenyu Qu & Wenbo Wang, 2023. "Photovoltaic Power Prediction Based on VMD-BRNN-TSP," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1033-:d:1072620
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    References listed on IDEAS

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    3. Mohamed Mohana & Abdelaziz Salah Saidi & Salem Alelyani & Mohammed J. Alshayeb & Suhail Basha & Ali Eisa Anqi, 2021. "Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning," Energies, MDPI, vol. 14(20), pages 1-18, October.
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    Cited by:

    1. Shengli Wang & Xiaolong Guo & Tianle Sun & Lihui Xu & Jinfeng Zhu & Zhicai Li & Jinjiang Zhang, 2025. "Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model," Energies, MDPI, vol. 18(2), pages 1-17, January.

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