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Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory

Author

Listed:
  • Lan Cao

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Haoyu Yang

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Chenggong Zhou

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
    State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

  • Shaochi Wang

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Yingang Shen

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Binxia Yuan

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

Abstract

To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. The ICEEMDAN algorithm reduces the instability of the environmental factor sequence. The KPCA algorithm reduces the input dimensions of the model. LSTM performs dynamic time modeling of the multivariate feature sequences to predict the output PV power. The adaptability of the ICEEMDAN-KPCA-LSTM model is assessed with datasets from a PV plant in west China and evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. Using 70% of the datasets for output PV power estimation, the results show a good performance, with an RMSE of 4.3715, MAPE of 8.9264%, and R-squared value of 89.973%. By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.

Suggested Citation

  • Lan Cao & Haoyu Yang & Chenggong Zhou & Shaochi Wang & Yingang Shen & Binxia Yuan, 2024. "Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory," Energies, MDPI, vol. 17(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6365-:d:1546562
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    References listed on IDEAS

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    1. Ali, Mumtaz & Prasad, Ramendra, 2019. "Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 281-295.
    2. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
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