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Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model

Author

Listed:
  • Yanhui Liu

    (Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China)

  • Jiulong Wang

    (Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China)

  • Lingyun Song

    (Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China)

  • Yicheng Liu

    (Suihua University Key Laboratory of Mechanical and Electrical Engineering Materials Preparation and Application, Suihua University, Suihua 152000, China)

  • Liqun Shen

    (School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

Accurate short-term photovoltaic (PV) power forecasting is crucial for ensuring the stability and efficiency of modern power systems, particularly given the intermittent and nonlinear characteristics of solar energy. This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. First, the original PV power signal was decomposed into intrinsic mode functions using a modified CEEMDAN method to better capture the complex nonlinear features. Subsequently, the fast Fourier transform and improved Pearson correlation coefficient (IPCC) were applied to identify and merge similar-frequency intrinsic mode functions, forming new composite components. Each reconstructed component was then forecasted individually using a BiLSTM model, whose parameters were optimized by the JS algorithm. Finally, the predicted components were aggregated to generate the final forecast output. Experimental results on real-world PV datasets demonstrate that the proposed CEEMDAN-JS-BiLSTM model achieves an R 2 of 0.9785, a MAPE of 8.1231%, and an RMSE of 37.2833, outperforming several commonly used forecasting models by a substantial margin in prediction accuracy. This highlights its effectiveness as a promising solution for intelligent PV power management.

Suggested Citation

  • Yanhui Liu & Jiulong Wang & Lingyun Song & Yicheng Liu & Liqun Shen, 2025. "Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model," Energies, MDPI, vol. 18(13), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3581-:d:1696533
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    References listed on IDEAS

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